CPLEX Parameters Reference - IBM

IBM ILOG CPLEX Optimization StudioCPLEX Parameters ReferenceVersion 12 Release 5

��

Copyright noticeDescribes general use restrictions and trademarks related to this document and the software described in this document.

© Copyright IBM Corp. 1987, 2013

US Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract withIBM Corp.

Trademarks

IBM, the IBM logo, ibm.com, WebSphere, and ILOG are trademarks or registered trademarks of International BusinessMachines Corp., in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or othercompanies. A current list of IBM trademarks is available on the Web at Copyright and trademark information.

Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of AdobeSystems Incorporated in the United States, and/or other countries.

Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.

UNIX is a registered trademark of The Open Group in the United States and other countries.

Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States,other countries, or both.

Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates.

Other company, product, or service names may be trademarks or service marks of others.

Further acknowledgments

IBM ILOG CPLEX states these additional registered trademarks, copyrights, and acknowledgments.

Additional registered trademarks, copyrights, licenses

Python is a registered trademark of the Python Software Foundation.

MATLAB is a registered trademark of The MathWorks, Inc.

OpenMPI is distributed by The Open MPI Project under the New BSD license and copyright 2004 - 2012.

MPICH2 is copyright 2002 by the University of Chicago and Argonne National Laboratory.

Acknowledgment of use: dtoa routine of the gdtoa package

IBM ILOG CPLEX acknowledges use of the dtoa routine of the gdtoa package, available at

http://www.netlib.org/fp/.

The author of this software is David M. Gay.

All Rights Reserved.

Copyright (C) 1998, 1999 by Lucent Technologies

Permission to use, copy, modify, and distribute this software and its documentation for any purpose and withoutfee is hereby granted, provided that the above copyright notice appears in all copies and that both that thecopyright notice and this permission notice and warranty disclaimer appear in supporting documentation, and thatthe name of Lucent or any of its entities not be used in advertising or publicity pertaining to distribution of thesoftware without specific, written prior permission.

LUCENT DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIEDWARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL LUCENT OR ANY OF ITSENTITIES BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGESWHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OFCONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITHTHE USE OR PERFORMANCE OF THIS SOFTWARE.

(end of acknowledgment of use of dtoa routine of the gdtoa package)

© Copyright IBM Corporation 1987, 2013.US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contractwith IBM Corp.

http://www.ibm.com/legal/copytrade.shtml

Contents

Chapter 1. Parameters of CPLEX . . . . 1Accessing parameters . . . . . . . . . . . 1Managing sets of parameters . . . . . . . . . 2Parameter names . . . . . . . . . . . . . 2Correspondence of parameters between APIs . . . 3Saving parameter settings to a file in the C API. . . 4

Chapter 2. Topical list of parameters . . 7Simplex . . . . . . . . . . . . . . . . 7Barrier . . . . . . . . . . . . . . . . 8MIP . . . . . . . . . . . . . . . . . 8MIP general . . . . . . . . . . . . . . 9MIP strategies . . . . . . . . . . . . . . 9MIP cuts . . . . . . . . . . . . . . . 10MIP tolerances . . . . . . . . . . . . . 11MIP limits . . . . . . . . . . . . . . . 11Solution polishing . . . . . . . . . . . . 12Solution pool . . . . . . . . . . . . . . 12Network . . . . . . . . . . . . . . . 12Parallel optimization . . . . . . . . . . . 12Sifting . . . . . . . . . . . . . . . . 13Preprocessing: aggregator, presolver . . . . . . 13Tolerances . . . . . . . . . . . . . . . 13Limits . . . . . . . . . . . . . . . . 14Display and output . . . . . . . . . . . . 15

Chapter 3. List of CPLEX parameters 17advanced start switch . . . . . . . . . . . 17constraint aggregation limit for cut generation. . . 18preprocessing aggregator fill. . . . . . . . . 18preprocessing aggregator application limit . . . . 19API string encoding switch . . . . . . . . . 20barrier algorithm . . . . . . . . . . . . 22barrier column nonzeros . . . . . . . . . . 23barrier crossover algorithm . . . . . . . . . 24barrier display information . . . . . . . . . 24convergence tolerance for LP and QP problems . . 25barrier growth limit . . . . . . . . . . . 25barrier iteration limit . . . . . . . . . . . 26barrier maximum correction limit . . . . . . . 27barrier objective range . . . . . . . . . . . 27barrier ordering algorithm . . . . . . . . . 28convergence tolerance for QC problems . . . . . 28barrier starting point algorithm . . . . . . . . 29MIP strategy best bound interval . . . . . . . 30bound strengthening switch . . . . . . . . . 30MIP branching direction . . . . . . . . . . 31backtracking tolerance . . . . . . . . . . . 32calculate QCP dual values . . . . . . . . . 33MIP cliques switch . . . . . . . . . . . . 34clock type for computation time . . . . . . . 35clone log in parallel optimization . . . . . . . 35coefficient reduction setting . . . . . . . . . 36variable (column) read limit . . . . . . . . . 37conflict information display . . . . . . . . . 38

MIP covers switch . . . . . . . . . . . . 38simplex crash ordering . . . . . . . . . . 39lower cutoff . . . . . . . . . . . . . . 40number of cutting plane passes . . . . . . . . 41row multiplier factor for cuts . . . . . . . . 41upper cutoff . . . . . . . . . . . . . . 42data consistency checking switch . . . . . . . 43dependency switch . . . . . . . . . . . . 43deterministic time limit . . . . . . . . . . 44MIP disjunctive cuts switch . . . . . . . . . 45MIP dive strategy . . . . . . . . . . . . 46dual simplex pricing algorithm . . . . . . . . 47type of cut limit . . . . . . . . . . . . . 47absolute MIP gap tolerance . . . . . . . . . 48relative MIP gap tolerance . . . . . . . . . 49integrality tolerance . . . . . . . . . . . 49epsilon used in linearization . . . . . . . . . 50Markowitz tolerance . . . . . . . . . . . 51optimality tolerance . . . . . . . . . . . 52perturbation constant . . . . . . . . . . . 52relaxation for FeasOpt . . . . . . . . . . . 53feasibility tolerance . . . . . . . . . . . . 54mode of FeasOpt . . . . . . . . . . . . 54file encoding switch . . . . . . . . . . . 56MIP flow cover cuts switch . . . . . . . . . 57MIP flow path cut switch. . . . . . . . . . 58feasibility pump switch . . . . . . . . . . 58candidate limit for generating Gomory fractionalcuts . . . . . . . . . . . . . . . . . 59MIP Gomory fractional cuts switch . . . . . . 60pass limit for generating Gomory fractional cuts . . 61MIP GUB cuts switch . . . . . . . . . . . 61MIP heuristic frequency . . . . . . . . . . 62MIP implied bound cuts switch. . . . . . . . 63MIP integer solution-file switch and prefix . . . . 63MIP integer solution limit . . . . . . . . . 65simplex maximum iteration limit . . . . . . . 65local branching heuristic . . . . . . . . . . 66MIP lift-and-project cuts switch. . . . . . . . 67MCF cut switch . . . . . . . . . . . . . 67memory reduction switch. . . . . . . . . . 68MIP callback switch between original model andreduced, presolved model . . . . . . . . . 69MIP node log display information . . . . . . . 70MIP emphasis switch . . . . . . . . . . . 71MIP node log interval . . . . . . . . . . . 72MIP kappa computation . . . . . . . . . . 73MIP priority order switch. . . . . . . . . . 75MIP priority order generation . . . . . . . . 75MIP dynamic search switch . . . . . . . . . 76MIQCP strategy switch . . . . . . . . . . 77MIP MIR (mixed integer rounding) cut switch . . . 78precision of numerical output in MPS and REW fileformats . . . . . . . . . . . . . . . . 79network logging display switch . . . . . . . 79network optimality tolerance . . . . . . . . 80

© Copyright IBM Corp. 1987, 2013 iii

network primal feasibility tolerance . . . . . . 81simplex network extraction level . . . . . . . 81network simplex iteration limit . . . . . . . . 82network simplex pricing algorithm . . . . . . 82MIP subproblem algorithm . . . . . . . . . 83node storage file switch . . . . . . . . . . 84MIP node limit . . . . . . . . . . . . . 85MIP node selection strategy . . . . . . . . . 85numerical precision emphasis . . . . . . . . 86nonzero element read limit . . . . . . . . . 87absolute objective difference cutoff . . . . . . 87lower objective value limit . . . . . . . . . 88upper objective value limit . . . . . . . . . 89parallel mode switch . . . . . . . . . . . 90simplex perturbation switch . . . . . . . . . 91simplex perturbation limit . . . . . . . . . 92deterministic time before starting to polish a feasiblesolution . . . . . . . . . . . . . . . 93absolute MIP gap before starting to polish a feasiblesolution . . . . . . . . . . . . . . . 94relative MIP gap before starting to polish a feasiblesolution . . . . . . . . . . . . . . . 94MIP integer solutions to find before starting topolish a feasible solution . . . . . . . . . . 95nodes to process before starting to polish a feasiblesolution . . . . . . . . . . . . . . . 96time before starting to polish a feasible solution . . 97time spent polishing a solution (deprecated) . . . 98maximum number of solutions generated forsolution pool by populate . . . . . . . . . 98primal simplex pricing algorithm . . . . . . . 99presolve dual setting . . . . . . . . . . . 100presolve switch. . . . . . . . . . . . . 101linear reduction switch . . . . . . . . . . 101limit on the number of presolve passes made. . . 102node presolve switch . . . . . . . . . . . 103simplex pricing candidate list size . . . . . . 104MIP probing level . . . . . . . . . . . . 104deterministic time spent probing . . . . . . . 105time spent probing . . . . . . . . . . . 105indefinite MIQP switch . . . . . . . . . . 106QP Q-matrix nonzero read limit . . . . . . . 107random seed . . . . . . . . . . . . . 107primal and dual reduction type . . . . . . . 108simplex refactoring frequency . . . . . . . . 109

relaxed LP presolve switch . . . . . . . . . 109relative objective difference cutoff . . . . . . 110number of attempts to repair infeasible MIP start 111MIP repeat presolve switch . . . . . . . . . 111RINS heuristic frequency . . . . . . . . . 112algorithm for continuous problems . . . . . . 113algorithm for continuous quadratic optimization 114MIP starting algorithm . . . . . . . . . . 115auxiliary root threads. . . . . . . . . . . 116constraint (row) read limit . . . . . . . . . 117scale parameter . . . . . . . . . . . . . 118messages to screen switch . . . . . . . . . 118sifting subproblem algorithm . . . . . . . . 119sifting information display . . . . . . . . . 120upper limit on sifting iterations . . . . . . . 120simplex iteration information display . . . . . 121simplex singularity repair limit . . . . . . . 121absolute gap for solution pool . . . . . . . . 122maximum number of solutions kept in solutionpool . . . . . . . . . . . . . . . . 123relative gap for solution pool . . . . . . . . 124solution pool intensity . . . . . . . . . . 124solution pool replacement strategy . . . . . . 126solution target type . . . . . . . . . . . 127MIP strong branching candidate list limit . . . . 128MIP strong branching iterations limit . . . . . 129limit on nodes explored when a subMIP is beingsolved . . . . . . . . . . . . . . . . 129symmetry breaking . . . . . . . . . . . 130global default thread count . . . . . . . . . 131optimizer time limit in seconds . . . . . . . 132tree memory limit . . . . . . . . . . . . 133deterministic tuning time limit . . . . . . . 134tuning information display . . . . . . . . . 136tuning measure. . . . . . . . . . . . . 136tuning repeater . . . . . . . . . . . . . 137tuning time limit in seconds . . . . . . . . 138MIP variable selection strategy . . . . . . . 139directory for working files . . . . . . . . . 140memory available for working storage . . . . . 141write level for MST, SOL files . . . . . . . . 142MIP zero-half cuts switch . . . . . . . . . 143

Index . . . . . . . . . . . . . . . 145

iv CPLEX Parameters Reference

Chapter 1. Parameters of CPLEX

Parameters, accessible and manageable by users, control the behavior of CPLEX.

Accessing parametersUsers access and modify parameters by means of methods in the various APIs.

Documentation about CPLEX parameters specific to the Python API is available asonline help inside a Python session. A brief introduction to CPLEX parameters isavailable in the topic Using CPLEX parameters in the CPLEX Python API in thetutorial about Python in Getting Stated with CPLEX.

Documentation about CPLEX parameters specific to the CPLEX for MATLABconnector is available in the topic Using parameters in the Getting Started withCPLEX for MATLAB manual. This information is also available as online helpinside a MATLAB session.

The following methods set and access parameters for objects of the class IloCplexin C++ and Java:setParamgetParamgetMingetMaxgetDefaultsetDefaults

The names of the corresponding accessors in the class Cplex in the .NET APIfollow the usual conventions of names and capitalization of languages in thatframework. For example, the class Cplex and its method Solve are denotedCplex.Solve. Likewise, the methods Cplex.GetParam and SetParam access and setparameters in the .NET API.

C applications and applications written in other languages callable from C accessand set parameters with the following routines:

CPXgetdblparam Accesses a parameter of type double

CPXsetdblparam Changes a parameter of type double

CPXinfodblparam Gets the default value and range of aparameter of type double

CPXgetintparam Accesses a parameter of type integer

CPXsetintparam Changes a parameter of type integer

CPXinfointparam Gets the default value and range of aparameter of type integer

CPXgetlongparam Accesses a parameter of type long

CPXsetlongparam Changes a parameter of type long

CPXinfolongparam Gets the default value and range of aparameter of type long

CPXgetstrparam Accesses a parameter of type string

CPXsetstrparam Changes a parameter of type string

© Copyright IBM Corp. 1987, 2013 1

CPXinfostrparam Gets the default value of a parameter oftype string

CPXsetdefaults Resets all parameters to their standarddefault values

CPXgetparamname Accesses the name of a parameter

CPXgetparamnum Access the identifying number assigned to aparameter

CPXgetchgparams Accesses all parameters not currently at theirdefault value

Managing sets of parametersUsers may groups parameters into sets in the object-oriented APIs.

The object-oriented APIs of CPLEX also allow you to group parameters into a setand then manage that set of parameters together.v In the C++ API, use the member functions of an instance of the class

IloCplex::ParameterSet.v In the Java API, use the methods of an object of the class

IloCplex.ParameterSet.v In the .NET API, use the methods of the class Cplex.ParameterSet.v In the Python API, use the methods of the class Cplex.ParameterSet.v In the CPLEX for MATLAB Toolbox API, use the function cplexoptimset.v In the MATLAB Cplex class API, use the structure Cplex.Param.

Parameter namesNames of CPLEX parameters follow the coding conventions of each API.

In the parameter table, each parameter has a name (that is, a symbolic constant) torefer to it within an application.v For the C API, these constants are capitalized and start with CPX_PARAM_; for

example, CPX_PARAM_ITLIM. They are used as the second argument in allparameter routines (except CPXsetdefaults, which does not require them).

v For C++ applications, the parameters are defined in nested enumeration typesfor Boolean, integer, floating-point, and string parameters. The enum names usemixed (lower and upper) case letters and must be prefixed with the class nameIloCplex:: for scope. For example, IloCplex::ItLim is the IloCplex equivalentof CPX_PARAM_ITLIM.

v For Java applications, the parameters are defined as final static objects in nestedclasses called IloCplex.BooleanParam, IloCplex.IntParam, IloCplex.LongParam,IloCplex.DoubleParam, and IloCplex.StringParam for Boolean, integer, long,floating-point, and string parameters, respectively. The parameter object namesuse mixed (lower and upper) case letters and must be prefixed with theappropriate class for scope. For example, IloCplex.IntParam.ItLim is the objectrepresenting the parameter CPX_PARAM_ITLIM.

v For .NET applications, the parameters follow the usual conventions forcapitalizing attributes and defining scope within a namespace.

v For Python applications, the names of parameters resemble the names in theCPLEX Interactive Optimizer, modified for the syntax of a Python application.

2 CPLEX Parameters Reference

For example, the command in the Interactive Optimizer set mip cuts mcfcutlooks like this in a Python application:cplex.parameters.mip.cuts.set(mcfcut).

v For MATLAB Cplex Class API applications, the names of parameters resemblethe names in the CPLEX Interactive Optimizer. For example, setting the MIPvariable selection strategy looks like this in a MATLAB Cplex Class APIapplication: cplex.Param.mip.strategy.variableselect = 3; where 3 indicatesstrong branching. The parameters in CPLEX for MATLAB Toolbox applicationsare named similarly. For example, setting the MIP variable selection strategylooks like this in a toolbox application:options.mip.strategy.variableselect.Cur = 3; where options is a structurecreated with the function cplexoptimset(’cplex’). In addition, in ordermaintain compatibility with the MATLAB Optimization Toolbox, a number ofparameters may be set using the MATLAB Optimization Toolbox parameternames; for example, the maximum nodes can be set with: options =cplexoptimset(’MaxNodes’, 400);.

The reference page of each parameter documents an integer that serves as areference number or unique identifier for each parameter. That integer referencenumber corresponds to the value that each symbolic constant represents, as foundin the cplex.h , cplexx.h , and cpxconst.h header files of the CallableLibrary (C API).

Tip:

Use the symbolic constant (that is, the name of the parameter) rather than itsreference number (that is, its unique identifier) for the sake of portability to futureversions of CPLEX.

Correspondence of parameters between APIsCertain specialized parameters of one API may not have an equivalent in anotherAPI.

Some parameters available for the C API are not supported as parameters for theobject-oriented APIs or have a slightly different name there. In particular:v “epsilon used in linearization” on page 50 (EpLin), the parameter specifying the

tolerance to use in linearization in the object oriented APIs (C++, Java, .NET), isnot applicable in the C API, nor in the Python API.

v “MIP callback switch between original model and reduced, presolved model” onpage 69(CPX_PARAM_MIPCBREDLP), the parameter specifying whether to use thereduced or original model in MIP callbacks, has no equivalent in theobject-oriented APIs (C++, Java, .NET) nor in the Python API, nor in theMATLAB connector.

v Logging output is controlled by a parameter in the C API (CPX_PARAM_SCRIND),but when using the object-oriented APIs, you control logging by configuring theoutput channel:– IloCplex::out in C++

For example, to turn off output to the screen, usecplex.setOut(env.getNullStream()) .

– IloCplex.output in JavaFor example, to turn off output to the screen, use cplex.setOut(null).

– Cplex.Out in .NETFor example, to turn off output to the screen, use Cplex.SetOut(Null).

Chapter 1. Parameters of CPLEX 3

– cplex.set_results_stream in PythonFor example, to turn off output to the screen, usecplex.set_results_stream(None).

– DisplayFunc in the MATLAB Cplex Class APIFor example, to turn off output to the screen, usecplex.DisplayFunc();.

– diagnostics and Diagnostics in the CPLEX for MATLAB Toolbox APIFor example, to turn off output to the screen, useoptimset(’Diagnostics’,’off’); or options.diagnostics = ’off’;.

v The parameter IloCplex::RootAlg in the C++ API corresponds to theseparameters in the C API:– “MIP starting algorithm” on page 115: CPX_PARAM_STARTALG– “algorithm for continuous problems” on page 113: CPX_PARAM_LPMETHOD– “algorithm for continuous quadratic optimization” on page 114:

CPX_PARAM_QPMETHOD

v The parameter IloCplex::NodeAlg in the C++ API corresponds to the parameter“MIP subproblem algorithm” on page 83 CPX_PARAM_SUBALG in the C API.

Saving parameter settings to a file in the C APIUsers of the Callable Library (C API) can save current parameter settings in a PRMfile.

You can read and write a file of parameter settings with the C API. The fileextension is .prm . The C routine CPXreadcopyparam reads parameter values from afile with the .prm extension. The routine CPXwriteparam writes a file of the currentnon-default parameter settings to a file with the .prm extension. Here is the formatof such a file:CPLEX Parameter File Version number

parameter_name parameter_value

Tip:

The heading with a version number in the first line of a PRM file is significant toCPLEX. An easy way to produce a correctly formatted PRM file with a properheading is to have CPLEX write the file for you.

CPLEX reads the entire file before changing any of the parameter settings. Aftersuccessfully reading a parameter file, the C API first sets all parameters to theirdefault value. Then it applies the settings it read in the parameter file. No changesare made if the parameter file contains errors, such as missing or illegal values.There is no checking for duplicate entries in the file. In the case of duplicateentries, the last setting in the file is applied.

When you create a parameter file from the C API, only the non-default values arewritten to the file. You can double-quote string values or not when you create aPRM file, but CPLEX always writes string-valued parameters with doublequotation marks.

The comment character in a parameter file is #. After that character, CPLEX ignoresthe rest of the line.


The C API issues a warning if the version recorded in the parameter file does notmatch the version of the product. A warning is also issued if a nonintegral value isgiven for an integer-valued parameter.

Here is an example of a correct CPLEX parameter file:CPLEX Parameter File Version 11.0CPX_PARAM_EPPER 3.45000000000000e-06CPX_PARAM_OBJULIM 1.23456789012345e+05CPX_PARAM_PERIND 1CPX_PARAM_SCRIND 1CPX_PARAM_WORKDIR "tmp"

Chapter 1. Parameters of CPLEX 5


Chapter 2. Topical list of parameters

Users can browse CPLEX parameters, organized by topics.

SimplexHere are links to parameters of interest to users of the simplex optimizers.

Selects the “algorithm for continuous problems” on page 113

“advanced start switch” on page 17

“lower objective value limit” on page 88

“upper objective value limit” on page 89

“dual simplex pricing algorithm” on page 47

“primal simplex pricing algorithm” on page 99

“simplex crash ordering” on page 39

“Markowitz tolerance” on page 51

“optimality tolerance” on page 52

“perturbation constant” on page 52

“simplex perturbation switch” on page 91

“simplex perturbation limit” on page 92

“relaxation for FeasOpt” on page 53

“feasibility tolerance” on page 54

“simplex maximum iteration limit” on page 65

“memory reduction switch” on page 68

“numerical precision emphasis” on page 86

“simplex pricing candidate list size” on page 104

“sifting subproblem algorithm” on page 119

“simplex iteration information display” on page 121

“simplex singularity repair limit” on page 121


BarrierHere are links to parameters of interest to users of the barrier optimizer.


“barrier algorithm” on page 22

“barrier starting point algorithm” on page 29

“barrier crossover algorithm” on page 24


“barrier ordering algorithm” on page 28

“barrier display information” on page 24

“barrier growth limit” on page 25

“barrier column nonzeros” on page 23

“barrier iteration limit” on page 26

“barrier maximum correction limit” on page 27

“barrier objective range” on page 27

“convergence tolerance for LP and QP problems” on page 25

“convergence tolerance for QC problems” on page 28



MIPHere are topics of interest to users of the MIP optimizer.

The parameters controlling MIP behavior are accessible through the followingtopics:v “MIP general” on page 9v “MIP strategies” on page 9v “MIP cuts” on page 10v “MIP tolerances” on page 11v “MIP limits” on page 11


MIP generalHere are links to parameters of general interest to users of the MIP optimizer.


“MIP emphasis switch” on page 71

“MIP repeat presolve switch” on page 111

“relaxed LP presolve switch” on page 109

“indefinite MIQP switch” on page 106

“solution target type” on page 127

“bound strengthening switch” on page 30



“MIP callback switch between original model and reduced, presolved model” onpage 69

“MIP node log display information” on page 70

“MIP node log interval” on page 72

“node storage file switch” on page 84

MIP strategiesHere are links to parameters controlling MIP strategies.

“MIP starting algorithm” on page 115

“MIP subproblem algorithm” on page 83

“MIP variable selection strategy” on page 139

“MIP strategy best bound interval” on page 30

“MIP branching direction” on page 31

“backtracking tolerance” on page 32

“MIP dive strategy” on page 46

“MIP heuristic frequency” on page 62

“local branching heuristic” on page 66

“MIP priority order switch” on page 75

“MIP priority order generation” on page 75

Chapter 2. Topical list of parameters 9

“MIP node selection strategy” on page 85

“node presolve switch” on page 103

“MIP probing level” on page 104

“RINS heuristic frequency” on page 112

“feasibility pump switch” on page 58

MIP cutsHere are links to parameters controlling cuts.

“constraint aggregation limit for cut generation” on page 18

“row multiplier factor for cuts” on page 41

“MIP cliques switch” on page 34

“MIP covers switch” on page 38

“MIP disjunctive cuts switch” on page 45

“MIP flow cover cuts switch” on page 57

“MIP flow path cut switch” on page 58

“MIP Gomory fractional cuts switch” on page 60

“MIP GUB cuts switch” on page 61

“MIP implied bound cuts switch” on page 63

“MIP lift-and-project cuts switch” on page 67

“MCF cut switch” on page 67

“MIP MIR (mixed integer rounding) cut switch” on page 78

“MIP zero-half cuts switch” on page 143

“pass limit for generating Gomory fractional cuts” on page 61

“candidate limit for generating Gomory fractional cuts” on page 59

“type of cut limit” on page 47

“number of cutting plane passes” on page 41


MIP tolerancesHere are links to parameters setting MIP tolerances.


“lower cutoff” on page 40

“upper cutoff” on page 42

“absolute objective difference cutoff” on page 87

“relative objective difference cutoff” on page 110

“absolute MIP gap tolerance” on page 48

“relative MIP gap tolerance” on page 49

“integrality tolerance” on page 49


MIP limitsHere are links to parameters setting MIP limits.

“MIP integer solution-file switch and prefix” on page 63

“pass limit for generating Gomory fractional cuts” on page 61

“candidate limit for generating Gomory fractional cuts” on page 59

“constraint aggregation limit for cut generation” on page 18

“type of cut limit” on page 47

“row multiplier factor for cuts” on page 41

“number of cutting plane passes” on page 41

“MIP node limit” on page 85

“time spent probing” on page 105

“number of attempts to repair infeasible MIP start” on page 111

“MIP strong branching candidate list limit” on page 128

“MIP strong branching iterations limit” on page 129

“limit on nodes explored when a subMIP is being solved” on page 129

“tree memory limit” on page 133


Solution polishingHere are links to parameters controlling starting conditions for solution polishing

“absolute MIP gap before starting to polish a feasible solution” on page 94

“relative MIP gap before starting to polish a feasible solution” on page 94

“MIP integer solutions to find before starting to polish a feasible solution” on page95

“nodes to process before starting to polish a feasible solution” on page 96

“time before starting to polish a feasible solution” on page 97

Solution poolHere are links to parameters controlling the solution pool.

“solution pool intensity” on page 124

“solution pool replacement strategy” on page 126

“maximum number of solutions generated for solution pool by populate” on page98

“maximum number of solutions kept in solution pool” on page 123

“absolute gap for solution pool” on page 122

“relative gap for solution pool” on page 124

NetworkHere are links to parameters of interest to users of the network flow optimizer.

“network optimality tolerance” on page 80

“network primal feasibility tolerance” on page 81

“simplex network extraction level” on page 81

“network simplex iteration limit” on page 82

“network simplex pricing algorithm” on page 82

“network logging display switch” on page 79

Parallel optimizationHere are links to parameters controlling parallel optimization.

“parallel mode switch” on page 90

“global default thread count” on page 131


SiftingHere are links to parameters of interest to users of the sifting optimizer.


“sifting information display” on page 120

“upper limit on sifting iterations” on page 120

Preprocessing: aggregator, presolverHere are links to parameters related to preprocessing.

“symmetry breaking” on page 130

“preprocessing aggregator fill” on page 18

“preprocessing aggregator application limit” on page 19

“bound strengthening switch” on page 30

“coefficient reduction setting” on page 36

“dependency switch” on page 43

“presolve dual setting” on page 100

“presolve switch” on page 101

“linear reduction switch” on page 101

“limit on the number of presolve passes made” on page 102

“node presolve switch” on page 103

“relaxed LP presolve switch” on page 109

“MIP repeat presolve switch” on page 111

“primal and dual reduction type” on page 108

TolerancesHere are links to parameters setting tolerances.

“convergence tolerance for LP and QP problems” on page 25

“convergence tolerance for QC problems” on page 28


“lower cutoff” on page 40

“upper cutoff” on page 42



“absolute MIP gap before starting to polish a feasible solution” on page 94


“relative MIP gap before starting to polish a feasible solution” on page 94

“integrality tolerance” on page 49

“epsilon used in linearization” on page 50

“Markowitz tolerance” on page 51

“optimality tolerance” on page 52

“network optimality tolerance” on page 80

“feasibility tolerance” on page 54



“relative objective difference cutoff” on page 110

“perturbation constant” on page 52

“absolute gap for solution pool” on page 122

“relative gap for solution pool” on page 124

LimitsHere are links to parameters setting general limits.

“memory available for working storage” on page 141

“global default thread count” on page 131

“optimizer time limit in seconds” on page 132

“variable (column) read limit” on page 37

“constraint (row) read limit” on page 117

“nonzero element read limit” on page 87

“QP Q-matrix nonzero read limit” on page 107


Display and outputHere are links to parameters controlling screen displays, logs, and files.

“messages to screen switch” on page 118

“tuning information display” on page 136

“barrier display information” on page 24

“simplex iteration information display” on page 121

“sifting information display” on page 120


“MIP node log interval” on page 72

“network logging display switch” on page 79

“clock type for computation time” on page 35

“conflict information display” on page 38

“data consistency checking switch” on page 43

“precision of numerical output in MPS and REW file formats” on page 79

“directory for working files” on page 140

“write level for MST, SOL files” on page 142



Chapter 3. List of CPLEX parameters

CPLEX parameters, documented here alphabetically by name in the CallableLibrary (C API), are available in the C++, Java, .NET, and Python APIs, as well asin the Interactive Optimizer, the MathWorks MATLAB connector, and the ExcelConnector.

advanced start switchIf set to 1 or 2, this parameter specifies that CPLEX should use advanced startinginformation when it initiates optimization.

Purpose

Advanced start switchC Name CPX_PARAM_ADVIND (int)

C++ Name AdvInd (int)

Java Name AdvInd (int)

.NET Name AdvInd (int)

OPL Name advind

Python Name advance

MATLAB Name advance

InteractiveOptimizer advance

Identifier 1001

Description

If set to 1 or 2, this parameter specifies that CPLEX should use advanced startinginformation when optimization is initiated.

For MIP models, setting 1 (one) will cause CPLEX to continue with a partiallyexplored MIP tree if one is available. If tree exploration has not yet begun, setting1 (one) specifies that CPLEX should use a loaded MIP start, if available. Setting 2retains the current incumbent (if there is one), re-applies presolve, and starts a newsearch from a new root.

Setting 2 is useful for continuous models. Consequently, it can be particularlyuseful for solving fixed MIP models, where a start vector but no correspondingbasis is available.

For continuous models solved with simplex, setting 1 (one) will use the currentlyloaded basis. If a basis is available only for the original, unpresolved model, or ifCPLEX has a start vector rather than a simplex basis, then the simplex algorithmwill proceed on the unpresolved model. With setting 2, CPLEX will first performpresolve on the model and on the basis or start vector, and then proceed withoptimization on the presolved problem.

For continuous models solved with the barrier algorithm, settings 1 or 2 willcontinue simplex optimization from the last available barrier iterate.

Tip:


If you optimize your MIP model, then change a tolerance, such as “upper cutoff”on page 42 (CPX_PARAM_CUTUP, CutUp), “lower cutoff” on page 40(CPX_PARAM_CUTLO, CutLo), “integrality tolerance” on page 49(CPX_PARAM_EPINT, EpInt), and then re-optimize, the change in tolerance maynot be taken into account in certain circumstances, depending on characteristics ofyour model and parameter settings. In order for CPLEX to take into account yourchange in tolerance, you must restart the second optimization from the beginning.That is, you must set CPX_PARAM_ADVIND, AdvInd to 0 (zero).

Table 1. Values

Value Meaning

0 Do not use advanced start information

1 Use an advanced basis supplied by the user;default

2 Crush an advanced basis or starting vectorsupplied by the user

constraint aggregation limit for cut generationLimits the number of constraints that can be aggregated for generating flow coverand mixed integer rounding (MIR) cuts.

Purpose

Constraint aggregation limit for cut generationC Name CPX_PARAM_AGGCUTLIM (int)

C++ Name AggCutLim (int)

Java Name AggCutLim (int)

.NET Name AggCutLim (int)

OPL Name aggcutlim

Python Name mip.limits.aggforcut

MATLAB Name mip.limits.aggforcut

Interactive Optimizer mip limits aggforcut

Identifier 2054

Description

Limits the number of constraints that can be aggregated for generating flow coverand mixed integer rounding (MIR) cuts.

Values

Any nonnegative integer; default: 3

preprocessing aggregator fillLimits variable substitutions by the aggregator.

Purpose

Preprocessing aggregator fillC Name 32-bit API CPX_PARAM_AGGFILL (CPXINT)


C Name 64-bit API CPX_PARAM_AGGFILL (CPXLONG)

C++ Name AggFill (CPXLONG)

Java Name AggFill (CPXLONG)

.NET Name AggFill (CPXLONG)

OPL Name aggfill

MATLAB Name preprocessing.fill

Python Name preprocessing.fill

Interactive Optimizer preprocessing fill

Identifier 1002

Description

Limits number of variable substitutions by the aggregator. If the net result of asingle substitution is more nonzeros than this value, the substitution is not made.

Tip:

The symbols CPXINT and CPXLONG declare a type of integer appropriate to yourspecification of a relatively small or large model by means of the symbolCPX_APIMODEL.

Values


preprocessing aggregator application limitInvokes the aggregator to use substitution where possible to reduce the number ofrows and columns before the problem is solved.

Purpose

Preprocessing aggregator application limitC Name CPX_PARAM_AGGIND (int)

C++ Name AggInd (int)

Java Name AggInd (int)

.NET Name AggInd (int)

OPL Name aggind

Python Name preprocessing.aggregator

MATLAB Name preprocessing.aggregator

Interactive Optimizer preprocessing aggregator

Identifier 1003

Description

Invokes the aggregator to use substitution where possible to reduce the number ofrows and columns before the problem is solved. If set to a positive value, theaggregator is applied the specified number of times or until no more reductionsare possible.

Chapter 3. List of CPLEX parameters 19

Table 2. Values

Value Meaning

-1 Automatic (1 for LP, infinite for MIP)default

0 Do not use any aggregator

Any positive integer Number of times to apply aggregator

API string encoding switchAPI string encoding switch

Purpose

API string encoding switchC Name CPX_PARAM_APIENCODING (string)

C++ Name APIEncoding (string)

Java Name not relevant

.NET Name not relevant

OPL Name

Python Name read.apiencoding

MATLAB Name read.apiencoding

Interactive Optimizer not relevant

Identifier 1130

Description

Specifies which encoding (also known as the code page) that CPLEX uses forstrings passed to and from routines of the Callable Library (C API) or methods ofthe C++ application programming interface (API) or methods of the Python API.That is, this parameter tells CPLEX which characters to expect as input and how torepresent as output such strings as the name of a model, of a variable, of aconstraint. If, for example, your C or C++ application uses an accent in the nameof a model, an umlaut in the name of a variable, or a Chinese character for thename of a constraint, then this parameter is of interest to you.

Note:

This parameter has no effect on IBM CPLEX Optimizer for z/OS, where onlyEBCDIC IBM-1047 encoding is available.

Which features does this parameter govern?

In the Callable Library (C API), this parameter specifies the encoding in whichCPLEX passes a string to a function destination added to a channel by means ofthe routine CPXaddfuncdest.

In the C++ API, this parameter specifies the encoding of streams accessed by themethods setWarning and setOut. CPLEX also encodes exceptions according to thevalue of this parameter.


In the Python API, this parameter specifies the encoding of streams accessed bymethods such as Cplex.set_log_stream, Cplex.set_warning_stream, orCplex.set_error_stream.

In the CPLEX for MATLAB APIs, the default value is the empty string ("").

Tip:

This parameter is not relevant to the Java API because CPLEX respects theencoding-conventions of Java. In fact, CPLEX relies on the encoding UTF-8 in Javaapplications. For a brief description of the advantages of UTF-8, see the topicSelecting an encoding in the CPLEX User’s Manual.

Which values does this parameter accept?

This parameter accepts a string specifying the user’s choice of encoding, such asUTF-8, ISO-8859-1, US-ASCII, and so forth. The acceptable values of this parameterdepend on the API.v In the Callable Library (C API), this parameter accepts any string that is the

name of a valid code page. For example, UTF-8 is a multi-byte encoding that isan acceptable value for this parameter; it encompasses the ASCII character set; itdoes not allow valid characters to include a NULL byte. If you use anothermulti-byte encoding, such as UTF-32 or UTF-16, for example, be sure to specifythe encoding fully by also including the byte order, like this: UTF-32BE orUTF-32LE.For a complete list of valid strings that are the name of an encoding (that is, thename of a code page), consult the web site of a standards organization such as:– A brief introduction to code pages– ICU: International Components for Unicode– International Components for Unicode at IBM

v In the C++ API, the value of this parameter must be the name of an encodingthat is a superset of ASCII. For example, ASCII is a subset of the encoding orcode page UTF-8, so UTF-8 is an acceptable value for this parameter. Likewise,ASCII is a subset of ISO-8859-1, also known as Latin-1, so ISO-8859-1 is anacceptable value for this parameter. However, the code page UTF-16 is notacceptable, nor is UTF-32 because both allow valid characters that contain aNULL byte.

v In the Python API, the value of this parameter cannot be the name of anencoding that allows a NULL byte within a valid character. In practice, thisstricture means that UTF-16 and UTF-32 are not acceptable values of thisparameter. Further restrictions depend on the version of Python that you areusing. Early versions of Python accepted a limited range of code pages. Recentversions of Python accept a greater variety of code pages. For more informationabout those choices dependent on Python, consult the documentation of yourPython installation and observe the stricture documented here about avoiding anencoding that contains NULL bytes within the representation of a character.

v In the .NET API, the only acceptable value of this parameter is its default valueISO-8859-1.

What is the default value of this parameter?

The default value of this parameter depends on the API.v In the Python API of CPLEX, the default value is the empty string (" ") for

consistency with Python conventions.


http://www.ibm.com/developerworks/library/codepages.html

http://site.icu-project.org/home

http://www-01.ibm.com/software/globalization/icu/index.html

v In the C API, the default value is the string ISO-8859-1 (also known as Latin-1).v In the C++ API, the default value is the string ISO-8859-1 (also known as

Latin-1).v In the .NET API, the only acceptable value of this parameter is its default value

ISO-8859-1.

The encoding ISO-8859-1 is a superset of the familiar ASCII encoding, so itsupports many widely used character sets. However, this default encoding cannotrepresent multi-byte character sets, such as Chinese, Japanese, Korean, Vietnamese,or Indian characters, for example. If you want to represent a character set thatrequires multiple bytes per character, then a better choice for the value of thisparameter is UTF-8.

If you change the value of this parameter, you must verify that your choice iscompatible with the “file encoding switch” on page 56 (CPX_PARAM_FILEENCODING,FileEncoding). In fact, the encoding or code page specified by the API encodingparameter must be a subset of the encoding or code page specified by the fileencoding parameter. For example, if the value of the file encoding parameter isUTF-8, then US-ASCII is an acceptable value of the API encoding parameter becauseUS-ASCII is a subset of UTF-8. For examples of the hazards of incompatible choicesof the encoding parameters, see the topic Selecting an encoding in the CPLEXUser’s Manual.

What about errors?

In situations where CPLEX encounters a string, such as content in a file, that is notcompatible with the specified encoding, the behavior is not defined. Because of theincompatibility, CPLEX silently converts the string to an inappropriate character ofthe specified encoding, or CPLEX raises the error CPXERR_ENCODING_CONVERSION. Foran example of why such unpredictable behavior occurs, see the topic Selecting anencoding in the CPLEX User’s Manual.

Values

valid string for the name of an encoding (code page) that is a superset of ASCII;default: ISO-8859-1 or empty string.

See also

“file encoding switch” on page 56

barrier algorithmThe default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1)when solving subproblems in a MIP problem, and the standard barrier algorithm(setting 3) in other cases.

Purpose

Barrier algorithmC Name CPX_PARAM_BARALG (int)

C++ Name BarAlg (int)

Java Name BarAlg (int)

.NET Name BarAlg (int)

OPL Name baralg


MATLAB Name barrier.algorithm

Python Name barrier.algorithm

Interactive Optimizer barrier algorithm

Identifier 3007

Description

The default setting 0 uses the "infeasibility - estimate start" algorithm (setting 1)when solving subproblems in a MIP problem, and the standard barrier algorithm(setting 3) in other cases. The standard barrier algorithm is almost always fastest.However, on problems that are primal or dual infeasible (common for MIPsubproblems), the standard algorithm may not work as well as the alternatives.The two alternative algorithms (settings 1 and 2) may eliminate numericaldifficulties related to infeasibility, but are generally slower.

Value Meaning

0 Default setting

1 Infeasibility-estimate start

2 Infeasibility-constant start

3 Standard barrier

barrier column nonzerosUsed in the recognition of dense columns.

Purpose

Barrier column nonzerosC Name CPX_PARAM_BARCOLNZ (int)

C++ Name BarColNz (int)

Java Name BarColNz (int)

.NET Name BarColNz (int)

OPL Name barcolnz

Python Name barrier.colnonzeros

MATLAB Name barrier.colnonzeros

Interactive Optimizer barrier colnonzeros

Identifier 3009

Description

Used in the recognition of dense columns. If columns in the presolved andaggregated problem exist with more entries than this value, such columns areconsidered dense and are treated specially by the CPLEX barrier optimizer toreduce their effect.

Value Meaning

0 Dynamically calculated; default

Any positive integer Number of nonzero entries that make acolumn dense


barrier crossover algorithmDecides which, if any, crossover is performed at the end of a barrier optimization.

Purpose

Barrier crossover algorithmC Name CPX_PARAM_BARCROSSALG (int)

C++ Name BarCrossAlg (int)

Java Name BarCrossAlg (int)

.NET Name BarCrossAlg (int)

OPL Name barcrossalg

Python Name barrier.crossover

MATLAB Name barrier.crossover

Interactive Optimizer barrier crossover

Identifier 3018

Description

Decides which, if any, crossover is performed at the end of a barrier optimization.This parameter applies when CPLEX uses the Barrier Optimizer to solve an LP orQP problem, or when it is used to solve the continuous relaxation of an MILP orMIQP at a node in a MIP.

By default, CPLEX does not invoke crossover on a QP problem. On an LP problem,it invokes primal and dual crossover in parallel when multiple threads areavailable.

Value Meaning

-1 No crossover

0 Automatic: let CPLEX choose; default

1 Primal crossover

2 Dual crossover

barrier display informationSets the level of barrier progress information to be displayed.

Purpose

Barrier display informationC Name CPX_PARAM_BARDISPLAY (int)

C++ Name BarDisplay (int)

Java Name BarDisplay (int)

.NET Name BarDisplay (int)

OPL Name bardisplay

Python Name barrier.display

MATLAB Name barrier.display

Interactive Optimizer barrier display

Identifier 3010


Description

Sets the level of barrier progress information to be displayed.

Value Meaning

0 No progress information

1 Normal setup and iteration information;default

2 Diagnostic information

convergence tolerance for LP and QP problemsSets the tolerance on complementarity for convergence.

Purpose

Convergence tolerance for LP and QP problemsC Name CPX_PARAM_BAREPCOMP (double)

C++ Name BarEpComp (double)

Java Name BarEpComp (double)

.NET Name BarEpComp (double)

OPL Name barepcomp

Python Name barrier.convergetol

MATLAB Name barrier.convergetol

Interactive Optimizer barrier convergetol

Identifier 3002

Description

Sets the tolerance on complementarity for convergence. The barrier algorithmterminates with an optimal solution if the relative complementarity is smaller thanthis value.

Changing this tolerance to a smaller value may result in greater numericalprecision of the solution, but also increases the chance of failure to converge in thealgorithm and consequently may result in no solution at all. Therefore, caution isadvised in deviating from the default setting.

Values

Any positive number greater than or equal to 1e-12; default: 1e-8.

See also

For problems with quadratic constraints (QCP), see “convergence tolerance for QCproblems” on page 28

barrier growth limitUsed to detect unbounded optimal faces.


Purpose

Barrier growth limitC Name CPX_PARAM_BARGROWTH (double)

C++ Name BarGrowth (double)

Java Name BarGrowth (double)

.NET Name BarGrowth (double)

OPL Name bargrowth

Python Name barrier.limits.growth

MATLAB Name barrier.limits.growth

Interactive Optimizer barrier limits growth

Identifier 3003

Description

Used to detect unbounded optimal faces. At higher values, the barrier algorithm isless likely to conclude that the problem has an unbounded optimal face, but morelikely to have numerical difficulties if the problem has an unbounded face.

Values

1.0 or greater; default: 1e12.

barrier iteration limitSets the number of barrier iterations before termination.

Purpose

Barrier iteration limitC Name CPX_PARAM_BARITLIM (long)

C++ Name BarItLim (long)

Java Name BarItLim (long)

.NET Name BarItLim (long)

OPL Name baritlim

Python Name barrier.limits.iteration

MATLAB Name barrier.limits.iteration

Interactive Optimizer barrier limits iterations

Identifier 3012

Description

Sets the number of barrier iterations before termination. When this parameter is setto 0 (zero), no barrier iterations occur, but problem setup occurs and informationabout the setup is displayed (such as Cholesky factor statistics).

Table 3. Values

Value Meaning

0 No barrier iterations

9223372036800000000 default

Any positive integer Number of barrier iterations beforetermination


barrier maximum correction limitSets the maximum number of centering corrections done on each iteration.

Purpose

Barrier maximum correction limitC Name CPX_PARAM_BARMAXCOR (long)

C++ Name BarMaxCor (long)

Java Name BarMaxCor (long)

.NET Name BarMaxCor (long)

OPL Name barmaxcor

Python Name barrier.limits.corrections

MATLAB Name barrier.limits.corrections

Interactive Optimizer barrier limits corrections

Identifier 3013

Description

Sets the maximum number of centering corrections done on each iteration. Anexplicit value greater than 0 (zero) may improve the numerical performance of thealgorithm at the expense of computation time.

Table 4. Values

Value Meaning

-1 Automatic; let CPLEX choose; default

0 None

Any positive integer Maximum number of centering correctionsper iteration

barrier objective rangeSets the maximum absolute value of the objective function.

Purpose

Barrier objective rangeC Name CPX_PARAM_BAROBJRNG (double)

C++ Name BarObjRng (double)

Java Name BarObjRng (double)

.NET Name BarObjRng (double)

OPL Name barobjrng

Python Name barrier.limits.objrange

MATLAB Name barrier.limits.objrange

Interactive Optimizer barrier limits objrange

Identifier 3004


Description

Sets the maximum absolute value of the objective function. The barrier algorithmlooks at this limit to detect unbounded problems.

Values

Any nonnegative number; default: 1e20

barrier ordering algorithmSets the algorithm to be used to permute the rows of the constraint matrix in orderto reduce fill in the Cholesky factor.

Purpose

Barrier ordering algorithmC Name CPX_PARAM_BARORDER (int)

C++ Name BarOrder (int)

Java Name BarOrder (int)

.NET Name BarOrder (int)

OPL Name barorder

Python Name barrier.ordering

MATLAB Name barrier.ordering

Interactive Optimizer barrier ordering

Identifier 3014

Description

Sets the algorithm to be used to permute the rows of the constraint matrix in orderto reduce fill in the Cholesky factor.

Table 5. Values

Value Meaning


1 Approximate minimum degree (AMD)

2 Approximate minimum fill (AMF)

3 Nested dissection (ND)

convergence tolerance for QC problemsSets the tolerance on complementarity for convergence in quadratically constrainedproblems (QCPs).

Purpose

Convergence tolerance for quadratically constrained problemsC Name CPX_PARAM_BARQCPEPCOMP (double)

C++ Name BarQCPEpComp (double)

Java Name BarQCPEpComp (double)

.NET Name BarQCPEpComp (double)


OPL Name barqcpepcomp

Python Name barrier.qcpconvergetol

MATLAB Name barrier.qcpconvergetol

Interactive Optimizer barrier qcpconvergetol

Identifier 3020

Description

Sets the tolerance on complementarity for convergence in quadratically constrainedproblems (QCPs). The barrier algorithm terminates with an optimal solution if therelative complementarity is smaller than this value.

Changing this tolerance to a smaller value may result in greater numericalprecision of the solution, but also increases the chance of a convergence failure inthe algorithm and consequently may result in no solution at all. Therefore, cautionis advised in deviating from the default setting.

Values


For LPs and for QPs (that is, when all the constraints are linear) see “convergencetolerance for LP and QP problems” on page 25 CPX_PARAM_BAREPCOMP, BarEpComp.

barrier starting point algorithmSets the algorithm to be used to compute the initial starting point for the barrieroptimizer.

Purpose

Barrier starting point algorithmC Name CPX_PARAM_BARSTARTALG (int)

C++ Name BarStartAlg (int)

Java Name BarStartAlg (int)

.NET Name BarStartAlg (int)

OPL Name barstartalg

Python Name barrier.startalg

MATLAB Name barrier.startalg

Interactive Optimizer barrier startalg

Identifier 3017

Description

Sets the algorithm to be used to compute the initial starting point for the barrieroptimizer.

Value Meaning

1 Dual is 0 (zero); default

2 Estimate dual

3 Average of primal estimate, dual 0 (zero)

4 Average of primal estimate, estimate dual


MIP strategy best bound intervalSets the best bound interval for MIP strategy.

Purpose

MIP strategy best bound intervalC Name CPX_PARAM_BBINTERVAL (long)

C++ Name BBInterval (long)

Java Name BBInterval (long)

.NET Name BBInterval (long)

OPL Name bbinterval

Python Name mip.strategy.bbinterval

MATLAB Name mip.strategy.bbinterval

Interactive Optimizer mip strategy bbinterval

Identifier 2039

Description

Sets the best bound interval for MIP strategy.

When you set this parameter to best estimate node selection, the best boundinterval is the interval at which the best bound node, instead of the best estimatenode, is selected from the tree. A best bound interval of 0 (zero) means “neverselect the best bound node.” A best bound interval of 1 (one) means “always selectthe best bound node,” and is thus equivalent to node select 1 (one).

Higher values of this parameter mean that the best bound node will be selectedless frequently; experience has shown it to be beneficial to select the best boundnode occasionally, and therefore the default value of this parameter is 7.

Table 6. Values

Value Meaning

0 Never select best bound node; always selectbest estimate

1 Always select best bound node

7 Select best bound node occasionally; default

Any positive integer Select best bound node less frequently thanbest estimate node

See also

“MIP node selection strategy” on page 85

bound strengthening switchDecides whether to apply bound strengthening in mixed integer programs (MIPs).

Purpose

Bound strengthening switchC Name CPX_PARAM_BNDSTRENIND (int)


C++ Name BndStrenInd (int)

Java Name BndStrenInd (int)

.NET Name BndStrenInd (int)

OPL Name bndstrenind

Python Name preprocessing.boundstrength

MATLAB Name preprocessing.boundstrength

Interactive Optimizer preprocessing boundstrength

Identifier 2029

Description

Decides whether to apply bound strengthening in mixed integer programs (MIPs).Bound strengthening tightens the bounds on variables, perhaps to the point wherethe variable can be fixed and thus removed from consideration during branch andcut.

Tip:

Strengthening means to replace one row of a model with another such that aninteger vector is feasible in the new row if and only if the integer vector wasfeasible in the original row. Strengthening improves the LP relaxation of the rowby finding a dominating row. In other words, the LP region defined by thestrengthened row plus the bounds on the variables will be strictly contained in theLP region defined by the original row plus bounds on the variables.

Value Meaning

-1 Automatic: let CPLEX choose; default

0 Do not apply bound strengthening

1 Apply bound strengthening

MIP branching directionDecides which branch, the up or the down branch, should be taken first at eachnode.

Purpose

MIP branching directionC Name CPX_PARAM_BRDIR (int)

C++ Name BrDir (int)

Java Name BrDir (int)

.NET Name BrDir (int)

OPL Name brdir

Python Name mip.strategy.branch

MATLAB Name mip.strategy.branch

Interactive Optimizer mip strategy branch

Identifier 2001

Description

Decides which branch, the up or the down branch, should be taken first at eachnode.


Value Symbol Meaning

-1 CPX_BRDIR_DOWN Down branch selected first

0 CPX_BRDIR_AUTO Automatic: let CPLEXchoose; default

1 CPX_BRDIR_UP Up branch selected first

backtracking toleranceControls how often backtracking is done during the branching process.

Purpose

Backtracking toleranceC Name CPX_PARAM_BTTOL (double)

C++ Name BtTol (double)

Java Name BtTol (double)

.NET Name BtTol (double)

OPL Name bttol

Python Name mip.strategy.backtrack

MATLAB Name mip.strategy.backtrack

Interactive Optimizer mip strategy backtrack

Identifier 2002

Description

Controls how often backtracking is done during the branching process. Thedecision when to backtrack depends on three values that change during the courseof the optimization:v the objective function value of the best integer feasible solution (incumbent);v the best remaining objective function value of any unexplored node (best node);v the objective function value of the most recently solved node (current objective).

If a cutoff tolerance (“upper cutoff” on page 42 or “lower cutoff” on page 40) hasbeen set by the user, then that value is used as the incumbent until an integerfeasible solution is found.

The target gap is defined to be the absolute value of the difference between theincumbent and the best node, multiplied by this backtracking parameter. CPLEXdoes not backtrack until the absolute value of the difference between the objectiveof the current node and the best node is at least as large as the target gap.

Low values of this backtracking parameter thus tend to increase the amount ofbacktracking, which makes the search process more of a pure best-bound search.Higher parameter values tend to decrease backtracking, making the search more ofa pure depth-first search.

The backtracking value has effect only after an integer feasible solution is found orwhen a cutoff has been specified. Note that this backtracking value merely permitsbacktracking but does not force it; CPLEX may choose to continue searching a limbof the tree if that limb seems a promising candidate for finding an integer feasiblesolution.


Values

Any number from 0.0 to 1.0; default: 0.9999

See also

“upper cutoff” on page 42, “lower cutoff” on page 40

calculate QCP dual valuesInstructs CPLEX to calculate the dual values of a quadratically constrainedproblem

Purpose

calculating QCP dual valuesC Name CPX_PARAM_CALCQCPDUALS (int)

C++ Name CalcQCPDuals (int)

Java Name CalcQCPDuals (int)

.NET Name CalcQCPDuals (int)

OPL Name

Python Name preprocessing.qcpduals

MATLAB Name preprocessing.qcpduals

Interactive Optimizer preprocessing qcpduals

Identifier 4003

Description

This parameter determines whether CPLEX preprocesses a quadraticallyconstrained program (QCP) so that the user can access dual values for the QCP.

If this parameter is set to 0 (zero), then CPLEX does not calculate dual values forthe QCP.

If this parameter is set to 1 (one), its default value, then CPLEX calculates dualvalues for the QCP as long as the calculations do not interfere with presolvereductions.

If this parameter is set to 2, then CPLEX calculates dual values and moreover,CPLEX disables any presolve reductions that interfere with these dual-valuecalculations.

For more information about accessing dual values of a QCP, see the topicAccessing dual values and reduced costs of QCP solutions in the CPLEX User'sManual.

For more information about presolve reductions, see the topic Advanced presolveroutines in the CPLEX User's Manual.


Values

Table 7. Values

Value Meaning Symbol inCallable Library(C API)

Symbol in C++,Java, .NET APIs

Symbol inPython API

0 Do not calculatedual values forthe QCP

CPX_QCPDUALS_NOQCPDualsNo no

1 Calculate dualvalues for theQCP as long asthe calculationsdo not interferewith presolvereductions.default

CPX_QCPDUALS_IFPOSSIBLEQCPDualsIfPossibleif_possible

2 Calculate dualvalues anddisable anypresolvereductions thatinterfere withthesecalculations.

CPX_QCPDUALS_FORCEQCPDualsForce force

MIP cliques switchDecides whether or not clique cuts should be generated for the problem.

Purpose

MIP cliques switchC Name CPX_PARAM_CLIQUES (int)

C++ Name Cliques (int)

Java Name Cliques (int)

.NET Name Cliques (int)

OPL Name cliques

Python Name mip.cuts.cliques

MATLAB Name mip.cuts.cliques

Interactive Optimizer mip cuts cliques

Identifier 2003

Description

Decides whether or not clique cuts should be generated for the problem. Settingthe value to 0 (zero), the default, specifies that the attempt to generate cliquesshould continue only if it seems to be helping.

For a definition of a clique cut, see the topic Clique cuts in the general topic Cutsin the CPLEX User’s Manual. The table Parameters for controlling cuts, also in theuser’s manual, includes links to the documentation of other parameters affectingother types of cuts.


Value Meaning

-1 Do not generate clique cuts


1 Generate clique cuts moderately

2 Generate clique cuts aggressively

3 Generate clique cuts very aggressively

clock type for computation timeDecides how computation times are measured for both reporting performance andterminating optimization when a time limit has been set.

Purpose

Clock type for computation timeC Name CPX_PARAM_CLOCKTYPE (int)

C++ Name ClockType (int)

Java Name ClockType (int)

.NET Name ClockType (int)

OPL Name clocktype

Python Name clocktype

MATLAB Name clocktype

Interactive Optimizer clocktype

Identifier 1006

Description

Decides how computation times are measured for both reporting performance andterminating optimization when a time limit has been set. Small variations inmeasured time on identical runs may be expected on any computer system withany setting of this parameter.

The default setting 2 supports wall clock time.

Value Meaning

0 Automatic: let CPLEX choose

1 CPU time

2 Wall clock time (total physical time elapsed);default

clone log in parallel optimizationSpecifies whether to create clone log files during parallel optimization.

Purpose

Creates clone log files in parallel optimizationC Name CPX_PARAM_CLONELOG

C++ Name CloneLog


Java Name CloneLog

.NET Name CloneLog

OPL Name

Python Name output.clonelog

MATLAB Name output.clonelog

Interactive Optimizer output clonelog

Identifier 1132

Description

Specifies whether CPLEX clones the log files of nodes during parallel or concurrentoptimization. When you use parallel or concurrent CPLEX, this feature makes itmore convenient to check node logs when you use more than one thread to solvemodels. For parallel optimization on N threads, for example, turning on thisparameter creates N logs,clone[0].log through clone[N-1].log. This feature isavailable only during concurrent optimization and mixed integer programming(MIP) optimization.

Value Meaning

-1 CPLEX does not clone log files. (off)

0 Automatic: CPLEX clones log files if log fileis specified. default

1 CPLEX clones log files. (on)

coefficient reduction settingDecides how coefficient reduction is used.

Purpose

Coefficient reduction settingC Name CPX_PARAM_COEREDIND (int)

C++ Name CoeRedInd (int)

Java Name CoeRedInd (int)

.NET Name CoeRedInd (int)

OPL Name coeredind

Python Name preprocessing.coeffreduce

MATLAB Name preprocessing.coeffreduce

Interactive Optimizer preprocessing coeffreduce

Identifier 2004

Description

Decides how coefficient reduction is used. Coefficient reduction improves theobjective value of the initial (and subsequent) LP relaxations solved during branchand cut by reducing the number of non-integral vertices. By default, CPLEXapplies coefficient reductions during preprocessing of a model.

The value 0 (zero) turns off coefficient reduction during preprocessing.

The value 1 (one) applies limited coefficient reduction to achieve only integralcoefficients.


The value 2, applies coefficient reduction somewhat more aggressively, reducing allcoefficients that can be reduced.

The value 3, the most aggressive setting of this parameter, applies a techniqueknown as tilting. Tilting can cut off additional fractional solutions in some models.Cutting off these fractional solutions potentially yields more progress in both thebest node and best integer solution in those particular models.

Tip:

Tilting means to replace one row of a model with another such that an integervector is feasible in the new row if and only if the integer vector was feasible inthe original row. In contrast to a dominating row, the LP region for a tilted row cancontain fractional points that are excluded by the LP region of the original row andthe bounds of the variables. But, the aim is to tilt the row in such a way that itgets tighter in those areas of the LP polyhedron that are not already covered byother constraints.

Value Meaning

-1 Automatic: let CPLEX decide; default

0 Do not use coefficient reduction

1 Reduce only to integral coefficients

2 Reduce all potential coefficients

3 Reduce aggressively with tilting

variable (column) read limitSpecifies a limit for the number of columns (variables) to read for an allocation ofmemory.

Purpose

Variable (column) read limitC Name CPX_PARAM_COLREADLIM (int)

C++ Name ColReadLim (int)

Java Name ColReadLim (int)

.NET Name ColReadLim (int)

Python Name read.variables

MATLAB Name read.variables

Interactive Optimizer read variables

Identifier 1023

Description

Specifies a limit for the number of columns (variables) to read for an allocation ofmemory.

This parameter does not restrict the size of a problem. Rather, it indirectly specifiesthe default amount of memory that will be pre-allocated before a problem is readfrom a file. If the limit is exceeded, more memory is automatically allocated.

Values


Any integer from 0 (zero) to CPX_BIGINT; default: 60 000.

conflict information displayDecides how much information CPLEX reports when the conflict refiner isworking.

Purpose

Conflict information displayC Name CPX_PARAM_CONFLICTDISPLAY (int)

C++ Name ConflictDisplay (int)

Java Name ConflictDisplay (int)

.NET Name ConflictDisplay (int)

OPL Name conflictdisplay

Python Name conflict.display

MATLAB Name conflict.display

Interactive Optimizer conflict display i

Identifier 1074

Description

Decides how much information CPLEX reports when the conflict refiner isworking.

Table 8. Values

Value Meaning

0 No display

1 Summary display; default

2 Detailed display

MIP covers switchDecides whether or not cover cuts should be generated for the problem.

Purpose

MIP covers switchC Name CPX_PARAM_COVERS (int)

C++ Name Covers (int)

Java Name Covers (int)

.NET Name Covers (int)

OPL Name covers

Python Name mip.cuts.covers

MATLAB Name mip.cuts.covers

Interactive Optimizer mip cuts covers

Identifier 2005


Description

Decides whether or not cover cuts should be generated for the problem. Setting thevalue to 0 (zero), the default, indicates that the attempt to generate covers shouldcontinue only if it seems to be helping.

For a definition of a cover cut, see the topic Cover cuts in the general topic Cuts,in the CPLEX User’s Manual. The table Parameters for controlling cuts, also in theuser’s manual, includes links to the documentation of other parameters affectingother types of cuts

Table 9. Values

Value Meaning

-1 Do not generate cover cuts


1 Generate cover cuts moderately

2 Generate cover cuts aggressively

3 Generate cover cuts very aggressively

simplex crash orderingDecides how CPLEX orders variables relative to the objective function whenselecting an initial basis.

Purpose

Simplex crash orderingC Name CPX_PARAM_CRAIND (int)

C++ Name CraInd (int)

Java Name CraInd (int)

.NET Name CraInd (int)

OPL Name craind

Python Name simplex.crash

MATLAB Name simplex.crash

Interactive Optimizer simplex crash

Identifier 1007

Description

Decides how CPLEX orders variables relative to the objective function whenselecting an initial basis.

Table 10. Values

Value Meaning

LP Primal

-1 Alternate ways of using objective coefficients

0 Ignore objective coefficients during crash

1 Alternate ways of using objectivecoefficients; default

LP Dual


Table 10. Values (continued)

Value Meaning

-1 Aggressive starting basis

0 Aggressive starting basis

1 Default starting basis; default

QP Primal

-1 Slack basis

0 Ignore Q terms and use LP solver for crash

1 Ignore objective and use LP solver for crash;default

QP Dual

-1 Slack basis

0 Use Q terms for crash

1 Use Q terms for crash; default

lower cutoffSets lower cutoff tolerance.

Purpose

Lower cutoffC Name CPX_PARAM_CUTLO (double)

C++ Name CutLo (double)

Java Name CutLo (double)

.NET Name CutLo (double)

OPL Name cutlo

Python Name mip.tolerances.lowercutoff

MATLAB Name mip.tolerances.lowercutoff

Interactive Optimizer mip tolerances lowercutoff

Identifier 2006

Description

Sets the lower cutoff tolerance. When the problem is a maximization problem,CPLEX cuts off or discards solutions that are less than the specified cutoff value. Ifthe model has no solution with an objective value greater than or equal to thecutoff value, then CPLEX declares the model infeasible. In other words, setting thelower cutoff value c for a maximization problem is similar to adding this constraintto the objective function of the model: obj >= c.

Tip:

This parameter is not effective with the conflict refiner nor with FeasOpt. That is,neither of those tools can analyze an infeasibility introduced by this parameter. Ifyou want to analyze such a condition, add an explicit objective constraint to yourmodel instead before you invoke either of those tools.

Values


Any number; default: -1e+75.

number of cutting plane passesSets the upper limit on the number of cutting plane passes CPLEX performs whensolving the root node of a MIP model.

Purpose

Number of cutting plane passesC Name CPX_PARAM_CUTPASS (long)

C++ Name CutPass (long)

Java Name CutPass (long)

.NET Name CutPass (long)

OPL Name cutpass

Python Name mip.limits.cutpasses

MATLAB Name mip.limits.cutpasses

Interactive Optimizer mip limits cutpasses

Identifier 2056

Description

Sets the upper limit on the number of cutting plane passes CPLEX performs whensolving the root node of a MIP model.

Table 11. Values

Value Meaning

-1 None


Any positive integer Number of passes to perform

row multiplier factor for cutsLimits the number of cuts that can be added.

Purpose

Row multiplier factor for cutsC Name CPX_PARAM_CUTSFACTOR (double)

C++ Name CutsFactor (double)

Java Name CutsFactor (double)

.NET Name CutsFactor (double)

OPL Name cutsfactor

Python Name mip.limits.cutsfactor

MATLAB Name mip.limits.cutsfactor

Interactive Optimizer mip limits cutsfactor

Identifier 2033


Description

Limits the number of cuts that can be added. The number of rows in the problemwith cuts added is limited to CutsFactor times the original number of rows. If theproblem is presolved, the original number of rows is that from the presolvedproblem.

A CutsFactor of 1.0 or less means that no cuts will be generated.

Because cuts can be added and removed during the course of optimization,CutsFactor may not correspond directly to the number of cuts seen in the node logor in the summary table at the end of optimization.

Values

Any nonnegative number; default: 4.0

upper cutoffSets the upper cutoff tolerance.

Purpose

Upper cutoffC Name CPX_PARAM_CUTUP (double)

C++ Name CutUp (double)

Java Name CutUp (double)

.NET Name CutUp (double)

OPL Name cutup

Python Name mip.tolerances.uppercutoff

MATLAB Name mip.tolerances.uppercutoff

Interactive Optimizer mip tolerances uppercutoff

Identifier 2007

Description

Sets the upper cutoff tolerance. When the problem is a minimization problem,CPLEX cuts off or discards any solutions that are greater than the specified uppercutoff value. If the model has no solution with an objective value less than orequal to the cutoff value, CPLEX declares the model infeasible. In other words,setting an upper cutoff value c for a minimization problem is similar to adding thisconstraint to the objective function of the model: obj <= c.

Tip:

This parameter is not effective with the conflict refiner nor with FeasOpt. That is,neither of those tools can analyze an infeasibility introduced by this parameter. Ifyou want to analyze such a condition, add an explicit objective constraint to yourmodel instead before you invoke either of those tools.

Values

Any number; default: 1e+75.


data consistency checking switchDecides whether data should be checked for consistency.

Purpose

Data consistency checking switchC Name CPX_PARAM_DATACHECK (int)

C++ Name DataCheck (bool)

Java Name DataCheck (bool)

.NET Name DataCheck (bool)

OPL Name datacheck

Python Name read.datacheck

MATLAB Name read.datacheck

Interactive Optimizer read datacheck

Identifier 1056

Description

Decides whether data should be checked for consistency. When this parameter ison, the routines CPXcopy____, CPXread____, and CPXchg____ of the C APIperform extensive checking of data in their array arguments, such as checking thatindices are within range, that there are no duplicate entries, and that values arevalid for the type of data or are valid numbers. This checking is useful fordebugging applications. When this checking identifies trouble, you can gathermore specific detail by calling one of the routines in check.c, as described in theCPLEX User's Manual in the topic Checking and debugging problem data Thoseroutines are documented in the group optim.cplex.callable.debug in the referencemanual of the Callable Library (C API).

Table 12. Values

int bool Symbol Meaning

0 false CPX_OFF Data checking off; donot check; default

1 true CPX_ON Data checking on

dependency switchDecides whether to activate the dependency checker.

Purpose

Dependency switchC Name CPX_PARAM_DEPIND (int)

C++ Name DepInd (int)

Java Name DepInd (int)

.NET Name DepInd (int)

OPL Name depind

Python Name preprocessing.dependency

MATLAB Name preprocessing.dependency

Interactive Optimizer preprocessing dependency


Identifier 1008

Description

Decides whether to activate the dependency checker. If on, the dependency checkersearches for dependent rows during preprocessing. If off, dependent rows are notidentified.

Table 13. Values

Value Meaning


0 Off: do not use dependency checker

1 Turn on only at the beginning ofpreprocessing

2 Turn on only at the end of preprocessing

3 Turn on at the beginning and at the end ofpreprocessing

deterministic time limitDeterministic time limit

Purpose

Deterministic time limitC Name CPX_PARAM_DETTILIM (double)

C++ Name DetTiLim (double)

Java Name DetTiLim (double)

.NET Name DetTiLim (double)

OPL Name

Python Name dettimelimit

MATLAB Name dettimelimit

Interactive Optimizer dettimelimit

Identifier 1127

Description

Sets a time limit expressed in ticks, a unit to measure work done deterministically.

The length of a deterministic tick may vary by platform. Nevertheless, ticks arenormally consistent measures for a given platform (combination of hardware andsoftware) carrying the same load. In other words, the correspondence of ticks toclock time depends on the hardware, software, and the current load of themachine. For the same platform and same load, the ratio of ticks per second staysroughly constant, independent of the model solved. However, for very shortoptimization runs, the variation of this ratio is typically high.

CPLEX measures deterministic time only for work inside CPLEX. In other words,deterministic time does not include user algorithms implemented by means ofcallbacks. However, each application programming interface (API) of CPLEX offersroutines or methods that access the deterministic clock and provide deterministictime stamps. In the APIs that support callbacks, you can use such deterministic


time stamps in your application to mark time even from callbacks. For more detailabout these deterministic time stamps, see the reference manual of the API thatyou use.v In the Callable Library (C API), see the documentation of CPXgetdettime and

CPXgetcallbackinfo.v In the C++ API, see the documentation of IloCplex::CallbackI::getStartDetTime

and IloCplex::CallbackI::getEndDetTime.v In the Java API, see the documentation of IloCplex.Callback.getStartDetTime and

IloCplex.Callback.getEndDetTime.v In the .NET API, see the documentation of Cplex.ICallback.GetStartDetTime and

GetEndDetTime.v In the Python API, see the documentation of Callback.get_start_dettime and

Callback.get_end_dettime.v In the MATLAB connector, see the documentation of cplex.Solution.dettime.

At the end of optimization, the Interactive Optimizer displays the deterministictime spent to optimize the model as well as the ratio of ticks per second. Forexample, consider these lines, typical of output from the Interactive Optimizer:MIP - Integer optimal solution: Objective = 1.1580000000e+03Solution time = 2.81 sec. Iterations = 72793 Nodes = 2666Deterministic time = 1996.47 ticks (709.54 ticks/sec)

See also

For a nondeterministic time limit measured in seconds, see “optimizer time limit inseconds” on page 132 (CPX_PARAM_TILIM, TiLim).

For more detail about use of time limits, see the topic Timing interface in theCPLEX User's Manual.

Value

Any nonnegative double value in deterministic ticks; default:1.0E+75

MIP disjunctive cuts switchDecides whether or not disjunctive cuts should be generated for the problem.

Purpose

MIP disjunctive cuts switchC Name CPX_PARAM_DISJCUTS (int)

C++ Name DisjCuts (int)

Java Name DisjCuts (int)

.NET Name DisjCuts (int)

OPL Name disjcuts

Python Name mip.cuts.disjunctive

MATLAB Name mip.cuts.disjunctive

Interactive Optimizer mip cuts disjunctive

Identifier 2053


Description

Decides whether or not disjunctive cuts should be generated for the problem.Setting the value to 0 (zero), the default, specifies that the attempt to generatedisjunctive cuts should continue only if it seems to be helping.

For a definition of a disjunctive cut, see the topic Disjunctive cuts in the generaltopic Cuts in the CPLEX User’s Manual. The table Parameters for controlling cuts,also in the user’s manual, includes links to the documentation of other parametersaffecting other types of cuts.

Table 14. Values

Value Meaning

-1 Do not generate disjunctive cuts


1 Generate disjunctive cuts moderately

2 Generate disjunctive cuts aggressively

3 Generate disjunctive cuts very aggressively

MIP dive strategyControls the MIP dive strategy.

Purpose

MIP dive strategyC Name CPX_PARAM_DIVETYPE (int)

C++ Name DiveType (int)

Java Name DiveType (int)

.NET Name DiveType (int)

OPL Name divetype

Python Name mip.strategy.dive

MATLAB Name mip.strategy.dive

Interactive Optimizer mip strategy dive

Identifier 2060

Description

Controls the MIP dive strategy. The MIP traversal strategy occasionally performsprobing dives, where it looks ahead at both children nodes before deciding whichnode to choose. The default (automatic) setting lets CPLEX choose when toperform a probing dive, 1 (one) directs CPLEX never to perform probing dives, 2always to probe, 3 to spend more time exploring potential solutions that aresimilar to the current incumbent. Setting 2, always to probe, is helpful for findinginteger solutions.

Table 15. Values

Value Meaning


1 Traditional dive

2 Probing dive



Value Meaning

3 Guided dive

dual simplex pricing algorithmDecides the type of pricing applied in the dual simplex algorithm.

Purpose

Dual simplex pricing algorithmC Name CPX_PARAM_DPRIIND (int)

C++ Name DPriInd (int)

Java Name DPriInd (int)

.NET Name DPriInd (int)

OPL Name dpriind

Python Name simplex.dgradient

MATLAB Name simplex.dgradient

Interactive Optimizer simplex dgradient

Identifier 1009

Description

Decides the type of pricing applied in the dual simplex algorithm. The defaultpricing (0) usually provides the fastest solution time, but many problems benefitfrom alternate settings.

Table 16. Values


0 CPX_DPRIIND_AUTO Automatic: let CPLEX choose; default

1 CPX_DPRIIND_FULL Standard dual pricing

2 CPX_DPRIIND_STEEP Steepest-edge pricing

3 CPX_DPRIIND_FULL_STEEP Steepest-edge pricing in slack space

4 CPX_DPRIIND_STEEPQSTART Steepest-edge pricing, unit initial norms

5 CPX_DPRIIND_DEVEX devex pricing

See also

“candidate limit for generating Gomory fractional cuts” on page 59, “MIP Gomoryfractional cuts switch” on page 60, “pass limit for generating Gomory fractionalcuts” on page 61

type of cut limitSets a limit for each type of cut.

Purpose

Type of cut limitC Name CPX_PARAM_EACHCUTLIM (int)


C++ Name EachCutLim (int)

Java Name EachCutLim (int)

.NET Name EachCutLim (int)

OPL Name eachcutlim

Python Name mip.limits.eachcutlimit

MATLAB Name mip.limits.eachcutlimit

Interactive Optimizer mip limits eachcutlimit

Identifier 2102

Description

Sets a limit for each type of cut.

This parameter allows you to set a uniform limit on the number of cuts of eachtype that CPLEX generates. By default, the limit is the largest integer supported bya given platform; that is, there is no effective limit by default.

Tighter limits on the number of cuts of each type may benefit certain models. Forexample, a limit on each type of cut will prevent any one type of cut from beingcreated in such large number that the limit on the total number of all types of cutsis reached before other types of cuts have an opportunity to be created.

A setting of 0 (zero) means no cuts.

This parameter does not influence the number of Gomory cuts. For means tocontrol the number of Gomory cuts, see also the fractional cut parameters:v “candidate limit for generating Gomory fractional cuts” on page 59:

CPX_PARAM_FRACCAND, FracCand;v “MIP Gomory fractional cuts switch” on page 60: CPX_PARAM_FRACCUTS, FracCuts;v “pass limit for generating Gomory fractional cuts” on page 61:

CPX_PARAM_FRACPASS, FracPass.

Table 17. Values

Value Meaning

0 No cuts

Any positive number Limit each type of cut

2100000000 default

absolute MIP gap toleranceSets an absolute tolerance on the gap between the best integer objective and theobjective of the best node remaining.

Purpose

Absolute MIP gap toleranceC Name CPX_PARAM_EPAGAP (double)

C++ Name EpAGap (double)

Java Name EpAGap (double)

.NET Name EpAGap (double)

OPL Name epagap


Python Name mip.tolerances.absmipgap

MATLAB Name mip.tolerances.absmipgap

InteractiveOptimizer mip tolerances absmipgap

Identifier 2008

Description

Sets an absolute tolerance on the gap between the best integer objective and theobjective of the best node remaining. When this difference falls below the value ofthis parameter, the mixed integer optimization is stopped.

Values

Any nonnegative number; default: 1e-06.

relative MIP gap toleranceSets a relative tolerance on the gap between the best integer objective and theobjective of the best node remaining.

Purpose

Relative MIP gap toleranceC Name CPX_PARAM_EPGAP (double)

C++ Name EpGap (double)

Java Name EpGap (double)

.NET Name EpGap (double)

OPL Name epgap

Python Name mip.tolerances.mipgap

MATLAB Name mip.tolerances.mipgap

Interactive Optimizer mip tolerances mipgap

Identifier 2009

Description

When the value

|bestbound-bestinteger|/(1e-10+|bestinteger|)

falls below the value of this parameter, the mixed integer optimization is stopped.

For example, to instruct CPLEX to stop as soon as it has found a feasible integersolution proved to be within five percent of optimal, set the relative MIP gaptolerance to 0.05.

Values

Any number from 0.0 to 1.0; default: 1e-04.

integrality toleranceSpecifies the amount by which an integer variable can be different from an integerand still be considered feasible.


Purpose

Integrality toleranceC Name CPX_PARAM_EPINT (double)

C++ Name EpInt (double)

Java Name EpInt (double)

.NET Name EpInt (double)

OPL Name epint

Python Name mip.tolerances.integrality

MATLAB Name mip.tolerances.integrality in Cplex Class API

MATLAB Name mip.tolerances.integrality in Toolbox (CPLEX compatible)

MATLAB Name TolXInteger in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer mip tolerances integrality

Identifier 2010

Description

Specifies the amount by which an integer variable can be different from an integerand still be considered feasible.

A value of zero is permitted, and the optimizer will attempt to meet this tolerance.

However, in some models, computer round-off may still result in small, nonzerodeviations from integrality. If any of these deviations exceed the value of thisparameter, or exceed 1e-10 in the case where this parameter has been set to a valueless than that, a solution status of CPX_STAT_OPTIMAL_INFEAS will be returnedinstead of the usual CPX_STAT_OPTIMAL.

Values

Any number from 0.0 to 0.5; default: 1e-05.

epsilon used in linearizationSets the epsilon (degree of tolerance) used in linearization in the object-orientedAPIs.

Purpose

Epsilon used in linearizationC Name CPX_PARAM_EPLIN but not applicable in the C API

C++ Name EpLin (double)

Java Name EpLin (double)

.NET Name EpLin (double)

Python Name not available in this API

MATLAB Name not available in this API

Interactive Optimizer not available in the Interactive Optimizer

Identifier 2068

Description

Sets the epsilon (degree of tolerance) used in linearization in the object-orientedAPIs.


Not applicable in the C API.

Not available in the Interactive Optimizer.

This parameter controls how strict inequalities are managed during linearization.In other words, it provides an epsilon for deciding when two values are not equalduring linearization. For example, when x is a numeric variable (that is, aninstance of IloNumVar ),

x < a

becomes

x <= a-eplin .

Similarly, x!=a

becomes

{(x < a) || (x > a)}

which is linearized automatically for you in the object-oriented APIs as

{( x <= a-eplin) || (x >= a+eplin)} .

Exercise caution in changing this parameter from its default value: the smaller theepsilon, the more numerically unstable the model will tend to become. If you arenot getting an expected solution for an object-oriented model that useslinearization, it might be that this solution is cut off because of the relatively highEpLin value. In such a case, carefully try reducing it.

Values

Any positive value greater than zero; default: 1e-3.

Markowitz toleranceInfluences pivot selection during basis factoring.

Purpose

Markowitz toleranceC Name CPX_PARAM_EPMRK (double)

C++ Name EpMrk (double)

Java Name EpMrk (double)

.NET Name EpMrk (double)

OPL Name epmrk

Python Name simplex.tolerances.markowitz

MATLAB Name simplex.tolerances.markowitz

Interactive Optimizer simplex tolerances markowitz

Identifier 1013


Description

Influences pivot selection during basis factoring. Increasing the Markowitzthreshold may improve the numerical properties of the solution.

Values

Any number from 0.0001 to 0.99999; default: 0.01.

optimality toleranceInfluences the reduced-cost tolerance for optimality.

Purpose

Optimality toleranceC Name CPX_PARAM_EPOPT (double)

C++ Name EpOpt (double)

Java Name EpOpt (double)

.NET Name EpOpt (double)

OPL Name epopt

Python Name simplex.tolerances.optimality

MATLAB Name simplex.tolerances.optimality in Cplex Class API

MATLAB Name simplex.tolerances.optimality in Toolbox (CPLEX compatible)

MATLAB Name TolFun and TolRLPFun in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer simplex tolerances optimality

Identifier 1014

Description

Influences the reduced-cost tolerance for optimality. This parameter governs howclosely CPLEX must approach the theoretically optimal solution.

The simplex algorithm halts when it has found a basic feasible solution with allreduced costs nonnegative. CPLEX uses this optimality tolerance to make thedecision of whether or not a given reduced cost should be considered nonnegative.CPLEX considers "nonnegative" a negative reduced cost having absolute value lessthan the optimality tolerance. For example, if your optimality tolerance is set to1e-6, then CPLEX considers a reduced cost of -1e-9 as nonnegative for the purposeof deciding whether the solution is optimal.

Values

Any number from 1e-9 to 1e-1; default: 1e-06.

perturbation constantSets the amount by which CPLEX perturbs the upper and lower bounds orobjective coefficients on the variables when a problem is perturbed in the simplexalgorithm.


Purpose

Perturbation constantC Name CPX_PARAM_EPPER (double)

C++ Name EpPer (double)

Java Name EpPer (double)

.NET Name EpPer (double)

OPL Name epper

Python Name simplex.limits.perturbation

MATLAB Name simplex.limits.perturbation

Interactive Optimizer simplex limits perturbation

Identifier 1015

Description

Sets the amount by which CPLEX perturbs the upper and lower bounds orobjective coefficients on the variables when a problem is perturbed in the simplexalgorithm. This parameter can be set to a smaller value if the default value createstoo large a change in the problem.

Values


relaxation for FeasOptControls the amount of relaxation for the routine CPXfeasopt in the C API or forthe method feasOpt in the object-oriented APIs.

Purpose

Relaxation for feasOpt

C Name CPX_PARAM_EPRELAX (double)

C++ Name EpRelax (double)

Java Name EpRelax (double)

.NET Name EpRelax (double)

OPL Name eprelax

Python Name feasopt.tolerance

MATLAB Name feasopt.tolerance

Interactive Optimizer feasopt tolerance

Identifier 2073

Description

Controls the amount of relaxation for the routine CPXfeasopt in the C API or forthe method feasOpt in the object-oriented APIs.

In the case of a MIP, it serves the purpose of the absolute gap for the feasOptmodel in Phase I (the phase to minimize relaxation).

Using this parameter, you can implement other stopping criteria as well. To do so,first call feasOpt with the stopping criteria that you prefer; then set this parameterto the resulting objective of the Phase I model; unset the other stopping criteria,


and call feasOpt again. Since the solution from the first call already matches thisparameter, Phase I will terminate immediately in this second call to feasOpt, andPhase II will start.

In the case of an LP, this parameter controls the lower objective limit for Phase I offeasOpt and is thus relevant only when the primal optimizer is in use.

Values

Any nonnegative value; default: 1e-6.

See also

“lower objective value limit” on page 88

feasibility toleranceSpecifies the feasibility tolerance, that is, the degree to which the basic variables ofa model may violate their bounds.

Purpose

Feasibility toleranceC Name CPX_PARAM_EPRHS (double)

C++ Name EpRHS (double)

Java Name EpRHS (double)

.NET Name EpRHS (double)

OPL Name eprhs

Python Name simplex.tolerances.feasibility

MATLAB Name simplex.tolerances.feasibility

Interactive Optimizer simplex tolerances feasibility

Identifier 1016

Description

Specifies the feasibility tolerance, that is, the degree to which values of the basicvariables calculated by the simplex method may violate their bounds. Feasibilityinfluences the selection of an optimal basis and can be reset to a higher valuewhen a problem is having difficulty maintaining feasibility during optimization.You may also wish to lower this tolerance after finding an optimal solution if thereis any doubt that the solution is truly optimal. If the feasibility tolerance is set toolow, CPLEX® may falsely conclude that a problem is infeasible. If you encounterreports of infeasibility during Phase II of the optimization, a small adjustment inthe feasibility tolerance may improve performance.

Values


mode of FeasOptDecides how FeasOpt measures the relaxation when finding a minimal relaxationin an infeasible model.


Purpose

Mode of FeasOptC Name CPX_PARAM_FEASOPTMODE (int)

C++ Name FeasOptMode (int)

Java Name FeasOptMode (int)

.NET Name FeasOptMode (int)

OPL Name feasoptmode

Python Name feasopt.mode

MATLAB Name feasopt.mode

Interactive Optimizer feasopt mode

Identifier 1084

Description

Decides how FeasOpt measures the relaxation when finding a minimal relaxationin an infeasible model. FeasOpt works in two phases. In its first phase, it attemptsto minimize its relaxation of the infeasible model. That is, it attempts to find afeasible solution that requires minimal change. In its second phase, it finds anoptimal solution among those that require only as much relaxation as it foundnecessary in the first phase. Values of this parameter indicate two aspects toCPLEX:v whether to stop in phase one or continue to phase two andv how to measure the relaxation, according to one of the following criteria:

– as a sum of required relaxations;– as the number of constraints and bounds required to be relaxed;– as a sum of the squares of required relaxations.

Table 18. Values

Value Symbol Symbol (C API) Meaning

0 MinSum CPX_FEASOPT_MIN_SUM Minimize the sum of all requiredrelaxations in first phase only;default

1 OptSum CPX_FEASOPT_OPT_SUM Minimize the sum of all requiredrelaxations in first phase and executesecond phase to find optimumamong minimal relaxations

2 MinInf CPX_FEASOPT_MIN_INF Minimize the number of constraintsand bounds requiring relaxation infirst phase only

3 OptInf CPX_FEASOPT_OPT_INF Minimize the number of constraintsand bounds requiring relaxation infirst phase and execute second phaseto find optimum among minimalrelaxations

4 MinQuad CPX_FEASOPT_MIN_QUAD Minimize the sum of squares ofrequired relaxations in first phaseonly

5 OptQuad CPX_FEASOPT_OPT_QUAD Minimize the sum of squares ofrequired relaxations in first phaseand execute second phase to findoptimum among minimal relaxations


file encoding switchfile encoding switch

Purpose

File encoding switchC Name CPX_PARAM_FILEENCODING (string)

C++ Name FileEncoding (string)

Java Name FileEncoding (string)

.NET Name FileEncoding (string)

OPL Name

Python Name read.fileencoding

MATLAB Name read.fileencoding

Interactive Optimizer read fileencoding string

Identifier 1129

Description

Specifies which encoding (also known as the code page) that CPLEX uses forreading and writing files. This parameter accepts a string, such as UTF-8, UTF-16LE,ISO-8859-1, US-ASCII, and so forth, specifying the user’s choice of encoding forreading and writing files.

Note:

This parameter has no effect on IBM CPLEX Optimizer for z/OS, where onlyEBCDIC IBM-1047 encoding is available.

The default value of this parameter depends on the CPLEX component.v In the CPLEX connector for MATLAB, the default value of the file encoding

parameter is the empty string (" ") for consistency with MATLAB conventions.v In the Python API, the default value of the file encoding parameter is the empty

string (" ") for consistency with Python conventions.v In other APIs of CPLEX, such as the C, C++, Java, .NET API, the default value

of the file encoding parameter is the string ISO-8859-1 (also known as Latin-1).

The encoding ISO-8859-1 is a superset of the familiar ASCII encoding, so itsupports many widely used character sets. However, this default encoding cannotrepresent multi-byte character sets, such as Chinese, Japanese, or Koreancharacters, for example. If you want CPLEX to represent a character set thatrequires multiple bytes per character, then a better choice for the value of thisparameter is UTF-8. The encoding UTF-8 is compatible with ASCII encoding; itrepresents every character in Unicode; it does not include a NULL byte in a validcharacter; it does not require specification of big-end or little-end byte order; itdoes not require a byte-order mark. If you use another multi-byte encoding, suchas UTF-32 or UTF-16, for example, be sure to specify the encoding fully byincluding the byte order, like this: UTF-32LE or UTF-32BE.

When you change the value of this parameter, you also need to verify that the“API string encoding switch” on page 20 (CPX_PARAM_APIENCODING, APIEncoding) iscompatible. The encoding specified by the API encoding parameter must be asubset of the encoding specified by the file encoding parameter. For example, if the


API encoding parameter specifies US-ASCII, then UTF-8 is a reasonable choice forthe file encoding parameter because the code page US-ASCII is a subset of UTF-8.

For a complete list of valid strings that are the name of an encoding (that is, thename of a code page), consult the web site of a standards organization such as:v A brief introduction to code pagesv ICU: International Components for Unicodev International Components for Unicode at IBM

In situations where CPLEX encounters a string, such as content in a file, that is notcompatible with the specified encoding, the behavior is not defined. Because of theincompatibility, CPLEX silently converts the string to an inappropriate character ofthe specified encoding, or CPLEX raises the error CPXERR_ENCODING_CONVERSION.

Values

valid string for the name of an encoding (code page); default: ISO-8859-1 or theempty string (“ “)

See also

“API string encoding switch” on page 20

MIP flow cover cuts switchDecides whether or not to generate flow cover cuts for the problem.

Purpose

MIP flow cover cuts switchC Name CPX_PARAM_FLOWCOVERS (int)

C++ Name FlowCovers (int)

Java Name FlowCovers (int)

.NET Name FlowCovers (int)

OPL Name flowcovers

Python Name mip.cuts.flowcovers

MATLAB Name mip.cuts.flowcovers

Interactive Optimizer mip cuts flowcovers

Identifier 2040

Description

Decides whether or not to generate flow cover cuts for the problem. Setting thevalue to 0 (zero), the default, indicates that the attempt to generate flow cover cutsshould continue only if it seems to be helping.

For a definition of a flow cover cut, see the topic Flow cover cuts in the generaltopic Cuts in the CPLEX User’s Manual. The table Parameters for controlling cuts,also in the user’s manual, includes links to the documentation of other parametersaffecting other types of cuts.


http://www.ibm.com/developerworks/library/codepages.html

http://site.icu-project.org/home

http://www-01.ibm.com/software/globalization/icu/index.html

Table 19. Values

Value Meaning

-1 Do not generate flow cover cuts


1 Generate flow cover cuts moderately

2 Generate flow cover cuts aggressively

MIP flow path cut switchDecides whether or not flow path cuts should be generated for the problem.

Purpose

MIP flow path cut switchC Name CPX_PARAM_FLOWPATHS (int)

C++ Name FlowPaths (int)

Java Name FlowPaths (int)

.NET Name FlowPaths (int)

OPL Name flowpaths

Python Name mip.cuts.pathcut

MATLAB Name mip.cuts.pathcut

Interactive Optimizer mip cuts pathcut

Identifier 2051

Description

Decides whether or not flow path cuts should be generated for the problem.Setting the value to 0 (zero), the default, indicates that the attempt to generate flowpath cuts should continue only if it seems to be helping.

For a definition of a flow path cut, see the topic Flow path cuts in the general topicCuts in the CPLEX User’s Manual. The table Parameters for controlling cuts, also inthe user’s manual, includes links to the documentation of other parametersaffecting other types of cuts.

Table 20. Values

Value Meaning

-1 Do not generate flow path cuts


1 Generate flow path cuts moderately

2 Generate flow path cuts aggressively

feasibility pump switchTurns on or off the feasibility pump heuristic for mixed integer programming(MIP) models.


Purpose

Feasibility pump switchC Name CPX_PARAM_FPHEUR (int)

C++ Name FPHeur (int)

Java Name FPHeur (int)

.NET Name FPHeur (int)

OPL Name fpheur

Python Name mip.strategy.fpheur

MATLAB Name mip.strategy.fpheur

Interactive Optimizer mip strategy fpheur

Identifier 2098

Description

Turns on or off the feasibility pump heuristic for mixed integer programming(MIP) models.

At the default setting 0 (zero), CPLEX automatically chooses whether or not toapply the feasibility pump heuristic on the basis of characteristics of the model.The feasibility pump does not apply to models of the type mixed integerquadratically constrained programs (MIQCP).

To turn off the feasibility pump heuristic, set the parameter to -1 (minus one).

To turn on the feasibility pump heuristic, set the parameter to 1 (one) or 2.

If the parameter is set to 1 (one), the feasibility pump tries to find a feasiblesolution without taking the objective function into account.

If the parameter is set to 2, the heuristic usually finds solutions of better objectivevalue, but is more likely to fail to find a feasible solution.

For more detail about the feasibility pump heuristic, see research by Fischetti,Glover, and Lodi (2003, 2005), by Bertacco, Fischetti, and Lodi (2005), and byAchterberg and Berthold (2005, 2007).

Table 21. Values

Value Meaning

-1 Do not apply the feasibility pump heuristic


1 Apply the feasibility pump heuristic with anemphasis on finding a feasible solution

2 Apply the feasibility pump heuristic with anemphasis on finding a feasible solution witha good objective value

candidate limit for generating Gomory fractional cutsLimits the number of candidate variables for generating Gomory fractional cuts.


Purpose

Candidate limit for generating Gomory fractional cutsC Name CPX_PARAM_FRACCAND (int)

C++ Name FracCand (int)

Java Name FracCand (int)

.NET Name FracCand (int)

OPL Name fraccand

Python Name mip.limits.gomorycand

MATLAB Name mip.limits.gomorycand

Interactive Optimizer mip limits gomorycand

Identifier 2048

Description

Limits the number of candidate variables for generating Gomory fractional cuts.

Values

Any positive integer; default: 200.

MIP Gomory fractional cuts switchDecides whether or not Gomory fractional cuts should be generated for theproblem.

Purpose

MIP Gomory fractional cuts switchC Name CPX_PARAM_FRACCUTS (int)

C++ Name FracCuts (int)

Java Name FracCuts (int)

.NET Name FracCuts (int)

OPL Name fraccuts

Python Name mip.cuts.gomory

MATLAB Name mip.cuts.gomory

Interactive Optimizer mip cuts gomory

Identifier 2049

Description

Decides whether or not Gomory fractional cuts should be generated for theproblem. Setting the value to 0 (zero), the default, indicates that the attempt togenerate Gomory fractional cuts should continue only if it seems to be helping.

For a definition of a Gomory fractional cut, see the topic Gomory fractional cuts inthe general topic Cuts in the CPLEX User’s Manual. The table Parameters forcontrolling cuts, also in the user’s manual, includes links to the documentation ofother parameters affecting other types of cuts.


Table 22. Values

Value Meaning

-1 Do not generate Gomory fractional cuts


1 Generate Gomory fractional cuts moderately

2 Generate Gomory fractional cutsaggressively

pass limit for generating Gomory fractional cutsLimits the number of passes for generating Gomory fractional cuts.

Purpose

Pass limit for generating Gomory fractional cutsC Name CPX_PARAM_FRACPASS (long)

C++ Name FracPass (long)

Java Name FracPass (long)

.NET Name FracPass (long)

OPL Name fracpass

Python Name mip.limits.gomorypass

MATLAB Name mip.limits.gomorypass

Interactive Optimizer mip limits gomorypass

Identifier 2050

Description

Limits the number of passes for generating Gomory fractional cuts. At the defaultsetting of 0 (zero), CPLEX decides the number of passes to make. The parameter isignored if the Gomory fractional cut parameter (“MIP Gomory fractional cutsswitch” on page 60: CPX_PARAM_FRACCUTS, FracCuts) is set to a nonzero value.

Table 23. Values

Value Meaning


Any positive integer Number of passes to generate Gomoryfractional cuts

MIP GUB cuts switchDecides whether or not to generate GUB cuts for the problem.

Purpose

MIP GUB cuts switchC Name CPX_PARAM_GUBCOVERS (int)

C++ Name GUBCovers (int)

Java Name GUBCovers (int)

.NET Name GUBCovers (int)

OPL Name gubcovers


Python Name mip.cuts.gubcovers

MATLAB Name mip.cuts.gubcovers

Interactive Optimizer mip cuts gubcovers

Identifier 2044

Description

Decides whether or not to generate generalized upper bound (GUB) cover cuts forthe problem. Setting the value to 0 (zero), the default, indicates that the attempt togenerate GUB cuts should continue only if it seems to be helping.

For a definition of a GUB cover cut, see the topic Generalized upper bound (GUB)cover cutsin the general topic Cuts in the CPLEX User’s Manual. The tableParameters for controlling cuts, also in the user’s manual, includes links to thedocumentation of other parameters affecting other types of cuts.

Table 24. Values

Value Meaning

-1 Do not generate GUB cuts


1 Generate GUB cuts moderately

2 Generate GUB cuts aggressively

MIP heuristic frequencyDecides how often to apply the periodic heuristic.

Purpose

MIP heuristic frequencyC Name CPX_PARAM_HEURFREQ (long)

C++ Name HeurFreq (long)

Java Name HeurFreq (long)

.NET Name HeurFreq (long)

OPL Name heurfreq

Python Name mip.strategy.heuristicfreq

MATLAB Name mip.strategy.heuristicfreq

Interactive Optimizer mip strategy heuristicfreq

Identifier 2031

Description

Decides how often to apply the periodic heuristic. Setting the value to -1 turns offthe periodic heuristic. Setting the value to 0 (zero), the default, applies the periodicheuristic at an interval chosen automatically. Setting the value to a positive numberapplies the heuristic at the requested node interval. For example, setting thisparameter to 20 dictates that the heuristic be called at node 0, 20, 40, 60, etc.

For an introduction to heuristics in CPLEX, see the topic Applying heuristicsamongthe topics in Tuning performance features of the mixed integer optimizerin the


CPLEX User’s Manual. For more about other heuristics, see the topics in Heuristics(also in the CPLEX User’s Manual). There, the topic Node heuristic refersspecifically to this parameter.

Table 25. Values

Value Meaning

-1 None


Any positive integer Apply the periodic heuristic at thisfrequency

MIP implied bound cuts switchDecides whether or not to generate implied bound cuts for the problem.

Purpose

MIP implied bound cuts switchC Name CPX_PARAM_IMPLBD (int)

C++ Name ImplBd (int)

Java Name ImplBd (int)

.NET Name ImplBd (int)

OPL Name implbd

Python Name mip.cuts.implied

MATLAB Name mip.cuts.implied

Interactive Optimizer mip cuts implied

Identifier 2041

Description

Decides whether or not to generate implied bound cuts for the problem. Settingthe value to 0 (zero), the default, indicates that the attempt to generate impliedbound cuts should continue only if it seems to be helping.

For a definition of an implied bound cut, see the topic Implied bound cuts in thegeneral topic Cuts in the CPLEX User’s Manual. The table Parameters forcontrolling cuts, also in the user’s manual, includes links to the documentation ofother parameters affecting other types of cuts.

Table 26. Values

Value Meaning

-1 Do not generate implied bound cuts


1 Generate implied bound cuts moderately

Generate implied bound cuts aggressively

MIP integer solution-file switch and prefixMIP integer solution file switch and filename prefix.


Purpose

MIP integer solution-file switch and filename prefix.C Name CPX_PARAM_INTSOLFILEPREFIX (string)

C++ Name IntSolFilePrefix (string)

Java Name IntSolFilePrefix (string)

.NET Name IntSolFilePrefix (string)

OPL Name

Python Name output.intsolfileprefix

MATLAB Name output.intsolfileprefix

Interactive Optimizer output intsolfileprefix

Identifier 2143

Description

Decides whether CPLEX writes the current MIP incumbent integer solution to afile and (if so) sets a prefix for the name of that file.

By default, the value of this parameter is the empty string, and file-writing isturned off. When this parameter is set to a non empty string, CPLEX writes eachnew incumbent to a file at the time the MIP integer solution is found.

In addition to switching on the writing of a file of solutions, this parameter alsospecifies the prefix of the name of the file to use. The prefix can contain a relativeor absolute path. If the prefix does not contain a relative or absolute path, CPLEXwrites to a file in the “directory for working files” on page 140.

The complete file name of the file that CPLEX writes is PREFIX-NNNNN.sol, where:v PREFIX is the prefix specified by this parameter;v NNNNN is the sequence number of the solution; the sequence starts at 00001;v sol represents the solution file format, documented in the topic SOL file format:

solution files in the reference manual, File formats supported by CPLEX.

Note:

Existing files of the same name will be overwritten.

If the specified file cannot be written (for example, in case of lack of disk space, orno write access to the specified location), optimization stops with an error statuscode.

This parameter accepts a string as its value. If you change either the “API stringencoding switch” on page 20 or the “file encoding switch” on page 56 from theirdefault value to a multi-byte encoding where a NULL byte can occur within theencoding of a character, you must take into account the issues documented in thetopic Selecting an encoding in the CPLEX User's Manual. Especially consider thepossibility that a NULL byte occurring in the encoding of a character caninadvertently signal the termination of a string, such as a filename or directorypath, and thus provoke surprising or incorrect results.

Values

valid string for the prefix of a file name; default: ” “ (the empty string; that is, theswitch is off)


See also


MIP integer solution limitSets the number of MIP solutions to be found before stopping.

Purpose

MIP integer solution limitC Name CPX_PARAM_INTSOLLIM (long)

C++ Name IntSolLim (long)

Java Name IntSolLim (long)

.NET Name IntSolLim (long)

OPL Name intsollim

Python Name mip.limits.solutions

MATLAB Name mip.limits.solutions

Interactive Optimizer mip limits solutions

Identifier 2015

Description

Sets the number of MIP solutions to be found before stopping.

This integer solution limit does not apply to the populate procedure, whichgenerates solutions to store in the solution pool. For a limit on the number ofsolutions generated by populate, see the populate limit parameter: “maximumnumber of solutions generated for solution pool by populate” on page 98.

Values

Any positive integer strictly greater than zero; zero is not allowed; default:9223372036800000000.

See also

“maximum number of solutions generated for solution pool by populate” on page98

simplex maximum iteration limitSets the maximum number of simplex iterations to be performed before thealgorithm terminates without reaching optimality.

Purpose

Simplex maximum iteration limitC Name CPX_PARAM_ITLIM (long)

C++ Name ItLim (long)

Java Name ItLim (long)

.NET Name ItLim (long)

OPL Name itlim


Python Name simplex.limits.iterations

MATLAB Name simplex.limits.iterations in Cplex Class API

MATLAB Name simplex.limits.iterations in Toolbox (CPLEX compatible)

MATLAB Name MaxIter in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer simplex limits iterations

Identifier 1020

Description

Sets the maximum number of simplex iterations to be performed before thealgorithm terminates without reaching optimality. When set to 0 (zero), no simplexmethod iteration occurs. However, CPLEX factors the initial basis from whichsolution routines provide information about the associated initial solution.

Values

Any nonnegative integer; default: 9223372036800000000.

local branching heuristicControls whether CPLEX applies a local branching heuristic to try to improve newincumbents found during a MIP search.

Purpose

Local branching heuristicC Name CPX_PARAM_LBHEUR (int)

C++ Name LBHeur (bool)

Java Name LBHeur (bool)

.NET Name LBHeur (bool)

OPL Name lbheur

Python Name mip.strategy.lbheur

MATLAB Name mip.strategy.lbheur

Interactive Optimizer mip strategy lbheur

Identifier 2063

Description

Controls whether CPLEX applies a local branching heuristic to try to improve newincumbents found during a MIP search. By default, this parameter is off. If youturn it on, CPLEX will invoke a local branching heuristic only when it finds a newincumbent. If CPLEX finds multiple incumbents at a single node, the localbranching heuristic will be applied only to the last one found.

Table 27. Values

Value bool Symbol Meaning

0 false CPX_OFF Local branchingheuristic is off;default

1 true CPX_ON Apply localbranching heuristic tonew incumbent


MIP lift-and-project cuts switchDecides whether or not lift-and-project cuts are generated for the problem.

Purpose

MIP lift-and-project cuts switchC Name CPX_PARAM_LANDPCUTS (int)

C++ Name LiftProjCuts (int)

Java Name LiftProjCuts (int)

.NET Name LiftProjCuts (int)

OPL Name

Python Name mip.cuts.liftproj

MATLAB Name mip.cuts.liftproj

Interactive Optimizer mip cuts liftproj

Identifier 2152

Description

Decides whether or not lift-and-project cuts are generated for the problem. Settingthe value of this parameter to 0 (zero), the default, specifies that the attempt togenerate lift-and-project cuts should continue only if it seems to be helping.

For a brief definition of lift-and-project cuts, see the topic MIP lift-and-project cutsin the general topic Cuts in the CPLEX User’s Manual. That same topic alsoincludes a bibliography for further reading about lift-and-project cuts.

The table Parameters for controlling cuts, also in the user’s manual, includes linksto the documentation of other parameters affecting other types of cuts.

Table 28. Values

Value Meaning

-1 Do not generate lift-and-project cuts


1 Generate lift-and-project cuts moderately

2 Generate lift-and-project cuts aggressively

3 Generate lift-and-project cuts veryaggressively

MCF cut switchSwitches on or off generation of multi-commodity flow cuts in a MIP.

Purpose

Switches on or off generation of multi-commodity flow cuts in a MIP.C Name CPX_PARAM_MCFCUTS (int)

C++ Name MCFCuts (int)

Java Name MCFCuts (int)

.NET Name MCFCuts (int)

OPL Name mcfcuts


Python Name mip.cuts.mcfcut

MATLAB Name mip.cuts.mcfcut

Interactive Optimizer mip cuts mcfcut

Identifier 2134

Description

Specifies whether CPLEX should generate multi-commodity flow cuts in aproblem where CPLEX detects the characteristics of a multi-commodity flownetwork with arc capacities. By default, CPLEX decides whether or not to generatesuch cuts.

To turn off generation of such cuts, set this parameter to -1 (minus one).

CPLEX is able to recognize the structure of a network as represented in manyreal-world models. When it recognizes such a network structure, CPLEX is able togenerate cutting planes that usually help solve such problems. In this case, the cutsthat CPLEX generates state that the capacities installed on arcs pointing into acomponent of the network must be at least as large as the total flow demand of thecomponent that cannot be satisfied by flow sources within the component.

For a definition of a multi-commodity flow cut, see the topic Multi-commodityflow (MCF) cuts in the general topic Cuts in the CPLEX User’s Manual. The tableParameters for controlling cuts, also in the user’s manual, includes links to thedocumentation of other parameters affecting other types of cuts.

Table 29. Values

Value Meaning

-1 Turn off MCF cuts

0 Automatic: let CPLEX decide whether togenerate MCF cuts; default

1 Generate a moderate number of MCF cuts

2 Generate MCF cuts aggressively

memory reduction switchDirects CPLEX that it should conserve memory where possible.

Purpose

Reduces use of memoryC Name CPX_PARAM_MEMORYEMPHASIS (int)

C++ Name MemoryEmphasis (bool)

Java Name MemoryEmphasis (bool)

.NET Name MemoryEmphasis (bool)

OPL Name memoryemphasis

Python Name emphasis.memory

MATLAB Name emphasis.memory

Interactive Optimizer emphasis memory

Identifier 1082


Description

Directs CPLEX that it should conserve memory where possible. When you set thisparameter to its nondefault value, CPLEX will choose tactics, such as datacompression or disk storage, for some of the data computed by the simplex,barrier, and MIP optimizers. Of course, conserving memory may impactperformance in some models. Also, while solution information will be availableafter optimization, certain computations that require a basis that has been factored(for example, for the computation of the condition number Kappa) may beunavailable.

Table 30. Values


0 false CPX_OFF Off; do not conservememory; default

1 true CPX_ON On; conservememory wherepossible

MIP callback switch between original model and reduced, presolvedmodel

Controls whether your callback accesses node information of the original model(off) or node information of the reduced, presolved model (on, default).

Purpose

MIP callback switch between original model and reduced, presolved modelC Name CPX_PARAM_MIPCBREDLP (int)

C++ Name not available in this API

Java Name not available in this API

.NET Name not available in this API



Interactive Optimizer not available

Identifier 2055

Description

Controls whether your callback accesses node information of the original model(off) or node information of the reduced, presolved model (on, default); alsoknown as the MIP callback reduced LP parameter.

Advanced routines to control MIP callbacks (such as CPXgetcallbacklp ,CPXsetheuristiccallbackfunc , CPXsetbranchcallbackfunc ,CPXgetbranchcallbackfunc , CPXsetcutcallbackfunc ,CPXsetincumbentcallbackfunc , CPXgetcallbacksosinfo , CPXcutcallbackadd ,CPXcutcallbackaddlocal , and others) consider the setting of this parameter andaccess the original model or the reduced, presolved model accordingly.

The routine CPXgetcallbacknodelp is an exception: it always accesses the currentnode LP associated with the presolved model, regardless of the setting of thisparameter.


For certain routines, such as CPXcutcallbackadd , when you set the parameterCPX_PARAM_MIPCBREDLP to zero, you should also set CPX_PARAM_PRELINEAR to zero aswell.

In the C++, Java, .NET, Python, and MATLAB APIs of CPLEX, only the originalmodel is available to callbacks. In other words, this parameter is effective only forcertain advanced routines of the C API.

Table 31. Values


0 CPX_OFF Off: use original model

1 CPX_ON On: use reduced, presolvedmodel; default

MIP node log display informationDecides what CPLEX reports to the screen during mixed integer optimization(MIP).

Purpose

MIP node log display informationC Name CPX_PARAM_MIPDISPLAY (int)

C++ Name MIPDisplay (int)

Java Name MIPDisplay (int)

.NET Name MIPDisplay (int)

OPL Name mipdisplay

Python Name mip.display

MATLAB Name mip.display

Interactive Optimizer mip display

Identifier 2012

Description

Decides what CPLEX reports to the screen and records in a log during mixedinteger optimization (MIP).

The amount of information displayed increases with increasing values of thisparameter.v A setting of 0 (zero) causes no node log to be displayed until the optimal

solution is found.v A setting of 1 (one) displays an entry for each integer feasible solution found.

Each entry contains:– the value of the objective function;– the node count;– the number of unexplored nodes in the tree;– the current optimality gap.

v A setting of 2 also generates an entry at a frequency determined by the “MIPnode log interval” on page 72 parameter. At a lower frequency, the logadditionally displays elapsed time in seconds and deterministic time in ticks.

v A setting of 3 gives all the information of option 2 plus additional information:


– At the same frequency as option 2, the node log adds a line specifying thenumber of cutting planes added to the problem since the last node log linewas displayed; this additional line is omitted if the number of cuts addedsince the last log line is 0 (zero).

– Whenever a MIP start was successfully used to find a new incumbentsolution, that success is recorded in the node log. (This information aboutMIP starts is independent of the MIP interval frequency in option 2.)

– For each new incumbent that is found, the node log displays how much timein seconds and how many deterministic ticks elapsed since the beginning ofoptimization. (This information about elapsed time between new incumbentsis independent of the MIP interval frequency in option 2.)

v A setting of 4 additionally generates entries for the LP root relaxation accordingto the setting of the parameter to control the “simplex iteration informationdisplay” on page 121 (SimDisplay, CPX_PARAM_SIMDISPLAY).

v A setting of 5 additionally generates entries for the LP subproblems, alsoaccording to the setting of the parameter to control the “simplex iterationinformation display” on page 121 (SimDisplay, CPX_PARAM_SIMDISPLAY).

Table 32. Values

Value Meaning

0 No display until optimal solution has been found

1 Display integer feasible solutions

2 Display integer feasible solutions plus an entry at a frequency setby “MIP node log interval” on page 72; default

3 Display the number of cuts added since previous display;information about the processing of each successful MIP start;elapsed time in seconds and elapsed time in deterministic ticks forinteger feasible solutions

4 Display information available from previous options andinformation about the LP subproblem at root

5 Display information available from previous options andinformation about the LP subproblems at root and at nodes

See also

“MIP node log interval” on page 72, “simplex iteration information display” onpage 121, “network logging display switch” on page 79, and “messages to screenswitch” on page 118

MIP emphasis switchControls trade-offs between speed, feasibility, optimality, and moving bounds inMIP.

Purpose

MIP emphasis switchC Name CPX_PARAM_MIPEMPHASIS (int)

C++ Name MIPEmphasis (int)

Java Name MIPEmphasis (int)

.NET Name MIPEmphasis (int)

OPL Name mipemphasis


Python Name emphasis.mip

MATLAB Name emphasis.mip

Interactive Optimizer emphasis mip

Identifier 2058

Description

Controls trade-offs between speed, feasibility, optimality, and moving bounds inMIP.

With the default setting of BALANCED, CPLEX works toward a rapid proof of anoptimal solution, but balances that with effort toward finding high quality feasiblesolutions early in the optimization.

When this parameter is set to FEASIBILITY, CPLEX frequently will generate morefeasible solutions as it optimizes the problem, at some sacrifice in the speed to theproof of optimality.

When set to OPTIMALITY, less effort may be applied to finding feasible solutionsearly.

With the setting BESTBOUND, even greater emphasis is placed on provingoptimality through moving the best bound value, so that the detection of feasiblesolutions along the way becomes almost incidental.

When the parameter is set to HIDDENFEAS, the MIP optimizer works hard to findhigh quality feasible solutions that are otherwise very difficult to find, so considerthis setting when the FEASIBILITY setting has difficulty finding solutions ofacceptable quality.

Table 33. Values


0 CPX_MIPEMPHASIS_BALANCED Balance optimality and feasibility;default

1 CPX_MIPEMPHASIS_FEASIBILITY Emphasize feasibility overoptimality

2 CPX_MIPEMPHASIS_OPTIMALITY Emphasize optimality overfeasibility

3 CPX_MIPEMPHASIS_BESTBOUND Emphasize moving best bound

4 CPX_MIPEMPHASIS_HIDDENFEAS Emphasize finding hidden feasiblesolutions

MIP node log intervalControls the frequency of node logging when the MIP display parameter(CPX_PARAM_MIPDISPLAY, MIPDisplay) is set higher than 1 (one).

Purpose

MIP node log intervalC Name CPX_PARAM_MIPINTERVAL (long)

C++ Name MIPInterval (long)

Java Name MIPInterval (long)


.NET Name MIPInterval (long)

OPL Name mipinterval

Python Name mip.interval

MATLAB Name mip.interval in Cplex Class API

MATLAB Name mip.interval in Toolbox (CPLEX compatible)

MATLAB Name NodeDisplayInterval in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer mip interval

Identifier 2013

Description

Controls the frequency of node logging when the MIP display parameter (“MIPnode log display information” on page 70) is set higher than 1 (one). Frequencymust be an integer; it may be 0 (zero), positive, or negative.

By default, CPLEX displays new information in the node log during a MIP solveat relatively high frequency during the early stages of solving a MIP model, andadds lines to the log at progressively longer intervals as solving continues. In otherwords, CPLEX logs information frequently in the beginning and progressively lessoften as it works.

When the value is a positive integer n, CPLEX displays new incumbents, plus itdisplays a new line in the log every n nodes.

When the value is a negative integer n, CPLEX displays new incumbents, and thenegative value determines how much processing CPLEX does before it displays anew line in the node log. A negative value close to zero means that CPLEXdisplays new lines in the log frequently. A negative value far from zero means thatCPLEX displays new lines in the log less frequently. In other words, a negativevalue of this parameter contracts or dilates the interval at which CPLEX displaysinformation in the node log.

Table 34. Values

Value Meaning

n < 0 Display new incumbents, and display a log line frequently at thebeginning of solving and less frequently as solving progresses

0 (zero) automatic: let CPLEX decide the frequency to log nodes (default)

n > 0 Display new incumbents, and display a log line every n nodes

See also


MIP kappa computationSets the strategy for computing statistics about MIP kappa

Purpose

MIP kappa computationC Name CPX_PARAM_MIPKAPPASTATS (int)

C++ Name MIPKappaStats (int)

Java Name MIPKappaStats (int)


.NET Name MIPKappaStats (int)

OPL Name mipkappastats

Python Name mip.strategy.kappastats

MATLAB Name mip.strategy.kappastats

Interactive Optimizer mip strategy kappastats

Identifier 2137

Description

Sets the strategy for CPLEX to gather statistics about the MIP kappa ofsubproblems of a MIP.

What is MIP kappa?

MIP kappa summarizes the distribution of the condition number of the optimalbases CPLEX encountered during the solution of a MIP model. That summary maylet you know more about the numerical difficulties of your MIP model.

When can you compute MIP kappa?

Because MIP kappa (as a statistical distribution) requires CPLEX to compute thecondition number of the optimal bases of the subproblems during branch-and-cutsearch, you can compute the MIP kappa only when CPLEX solves the subproblemwith its simplex optimizer. In other words, in order to obtain results with thisparameter, you can not use the sifting optimizer nor the barrier without crossoverto solve the subproblems. See the parameters “MIP subproblem algorithm” onpage 83 (CPX_PARAM_SUBALG, NodeAlg) and “MIP starting algorithm” on page 115(CPX_PARAM_STARTALG, RootAlg) for more details about those choices.

What are the performance trade-offs for computing MIP kappa?

Computing the kappa of a subproblem has a cost. In fact, computing MIP kappafor the basis matrices can be computationally expensive and thus generally slowsdown the solution of a problem. Therefore,

the automatic setting CPX_MIPKAPPA_AUTO tells CPLEX generally not to computeMIP kappa, but in cases where the parameter “numerical precision emphasis” onpage 86 (CPX_PARAM_NUMERICALEMPHASIS, NumericalEmphasis) is turned on, that is,set to 1 (one), CPLEX computes MIP kappa for a sample of subproblems.

The value CPX_MIPKAPPA_SAMPLE leads to a negligible performance degradation onaverage, but can slow down the branch-and-cut exploration by as much as 10% oncertain models.

The value CPX_MIPKAPPA_FULL leads to a 2% performance degradation on average,but can significantly slow the branch-and-cut exploration on certain models.

In practice, the value CPX_MIPKAPPA_SAMPLE is a good trade-off betweenperformance and accuracy of statistics.

If you need very accurate statistics, then use value CPX_MIPKAPPA_FULL.

Table 35. Values


–1 CPX_MIPKAPPA_OFF No MIP kappa statistics




0 CPX_MIPKAPPA_AUTO Automatic: let CPLEXdecide; default

1 CPX_MIPKAPPA_SAMPLE Compute MIP kappa for asample of subproblems

2 CPX_MIPKAPPA_FULL Compute MIP kappa for allsubproblems

MIP priority order switchDecides whether to use the priority order, if one exists, for the next mixed integeroptimization.

Purpose

MIP priority order switchC Name CPX_PARAM_MIPORDIND (int)

C++ Name MIPOrdInd (bool)

Java Name MIPOrdInd (bool)

.NET Name MIPOrdInd (bool)

OPL Name mipordind

Python Name mip.strategy.order

MATLAB Name mip.strategy.order

Interactive Optimizer mip strategy order

Identifier 2020

Description

Decides whether to use the priority order, if one exists, for the next mixed integeroptimization.

Table 36. Values


false CPX_OFF Off: do not usepriority order

true CPX_ON On: use priorityorder, if it exists;default

MIP priority order generationSelects the type of generic priority order to generate when no priority order ispresent.

Purpose

MIP priority order generationC Name CPX_PARAM_MIPORDTYPE (int)

C++ Name MIPOrdType (int)

Java Name MIPOrdType (int)


.NET Name MIPOrdType (int)

OPL Name mipordtype

Python Name mip.ordertype

MATLAB Name mip.ordertype

Interactive Optimizer mip ordertype

Identifier 2032

Description

Selects the type of generic priority order to generate when no priority order ispresent.

Table 37. Values


0 default Do not generate a priorityorder

1 CPX_MIPORDER_COST Use decreasing cost

2 CPX_MIPORDER_BOUNDS Use increasing bound range

3 CPX_MIPORDER_SCALEDCOST Use increasing cost percoefficient count

MIP dynamic search switchSets the search strategy for a mixed integer program (MIP).

Purpose

MIP dynamic search switchC Name CPX_PARAM_MIPSEARCH (int)

C++ Name MIPSearch (int)

Java Name MIPSearch (int)

.NET Name MIPSearch (int)

OPL Name mipsearch

Python Name mip.strategy.search

MATLAB Name mip.strategy.search

Interactive Optimizer mip strategy search

Identifier 2109

Description

Sets the search strategy for a mixed integer program (MIP). By default, CPLEXchooses whether to apply dynamic search or conventional branch and cut based oncharacteristics of the model and the presence (or absence) of callbacks.

Only informational callbacks are compatible with dynamic search. For more detailabout informational callbacks and how to create and install them in yourapplication, see Informational callbacks in the CPLEX User’s Manual.

To benefit from dynamic search, a MIP must not include query callbacks. In otherwords, query callbacks are not compatible with dynamic search. For a moredetailed definition of query or diagnostic callbacks, see Query or diagnosticcallbacks in the CPLEX User’s Manual.


To benefit from dynamic search, a MIP must not include control callbacks (that is,callbacks that alter the search path through the solution space). In other words,control callbacks are not compatible with dynamic search. These control callbacksare identified as advanced in the reference manuals of the APIs. If control callbacksare present in your application, CPLEX will disable dynamic search, issue awarning, and apply only static branch and cut. If you want to control the searchyourself, for example, through advanced control callbacks, then you should set thisparameter to 1 (one) to disable dynamic search and to apply conventional branchand cut.

Table 38. Values

Value Symbolic Name Meaning

0 CPX_MIPSEARCH_AUTO Automatic: let CPLEXchoose; default

1 CPX_MIPSEARCH_TRADITIONAL Apply traditional branch andcut strategy; disable dynamicsearch

2 CPX_MIPSEARCH_DYNAMIC Apply dynamic search

MIQCP strategy switchSets the strategy that CPLEX uses to solve a quadratically constrained mixedinteger program (MIQCP).

Purpose

MIQCP strategy switchC Name CPX_PARAM_MIQCPSTRAT (int)

C++ Name MIQCPStrat (int)

Java Name MIQCPStrat (int)

.NET Name MIQCPStrat (int)

OPL Name miqcpstrat

Python Name mip.strategy.miqcpstrat

MATLAB Name mip.strategy.miqcpstrat

Interactive Optimizer mip strategy miqcpstrat

Identifier 2110

Description

Sets the strategy that CPLEX uses to solve a quadratically constrained mixedinteger program (MIQCP).

This parameter controls how MIQCPs ( that is, mixed integer programs with oneor more constraints including quadratic terms) are solved. For more detail aboutthe types of quadratically constrained models that CPLEX solves, see Identifying aquadratically constrained program (QCP) in the CPLEX User’s Manual.

At the default setting of 0 (zero), CPLEX automatically chooses a strategy.

When you set this parameter to the value 1 (one), you tell CPLEX to solve a QCPrelaxation of the model at each node.


When you set this parameter to the value 2, you tell CPLEX to attempt to solve anLP relaxation of the model at each node.

CPLEX uses a linear approximation of the quadratic constraints, adding cone cutsas it proceeds. This approach has advantages that can yield better overallperformance, despite the disadvantage of approximating the quadratic constraints.First advantage: when you solve a QCP relaxation at each node, the barrier methodused to do the solve cannot take advantage of advanced start information. Incontrast, solving LP relaxations can use advanced starts, potentially making thenode relaxations run faster. Second advantage: the second order conetransformations used to solve the QCP relaxation occasionally create numericalinstabilities that make the QCP relaxation difficult to solve. LP relaxations requirefewer transformations. Also, LP relaxations can use either the barrier or simplexmethods, so they are less likely to have such issues.

For some models, the setting 2 may be more effective than 1 (one). You may needto experiment with this parameter to determine the best setting for your model.

Specifically, if the node log indicates long solve times for a QCP relaxation,consider setting this parameter to the value 2. Conversely, if you see that the bestnode value appears to move very slowly, the linear approximation may not beparticularly accurate; in such cases, setting the parameter to value 1 (one) mayimprove performance.

Table 39. Values

Value Meaning


1 Solve a QCP node relaxation at each node

2 Solve an LP node relaxation at each node

MIP MIR (mixed integer rounding) cut switchDecides whether or not to generate MIR cuts (mixed integer rounding cuts) for theproblem.

Purpose

MIP MIR (mixed integer rounding) cut switchC Name CPX_PARAM_MIRCUTS (int)

C++ Name MIRCuts (int)

Java Name MIRCuts (int)

.NET Name MIRCuts (int)

OPL Name mircuts

Python Name mip.cuts.mircut

MATLAB Name mip.cuts.mircut

Interactive Optimizer mip cuts mircut

Identifier 2052

Description

Decides whether or not to generate MIR cuts (mixed integer rounding cuts) for theproblem. The value 0 (zero), the default, specifies that the attempt to generate MIRcuts should continue only if it seems to be helping.


For a definition of a MIR cut, see the topic Mixed integer rounding (MIR) cuts inthe general topic Cuts in the CPLEX User’s Manual. The table Parameters forcontrolling cuts, also in the user’s manual, includes links to the documentation ofother parameters affecting other types of cuts.

Value Meaning

-1 Do not generate MIR cuts


1 Generate MIR cuts moderately

2 Generate MIR cuts aggressively

precision of numerical output in MPS and REW file formatsDecides the precision of numerical output in the MPS and REW file formats.

Purpose

Precision of numerical output in MPS and REW file formatsC Name CPX_PARAM_MPSLONGNUM (int)

C++ Name MPSLongNum (bool)

Java Name MPSLongNum (bool)

.NET Name MPSLongNum (bool)

Python Name output.mpslong

MATLAB Name output.mpslong

Interactive Optimizer output mpslong

Identifier 1081

Description

Decides the precision of numerical output in the MPS and REW file formats. Whenthis parameter is set to its default value 1 (one), numbers are written to MPS filesin full-precision; that is, up to 15 significant digits may be written. The setting 0(zero) writes files that correspond to the standard MPS format, where at most 12characters can be used to represent a value. This limit may result in loss ofprecision.


0 false CPX_OFF Off: use limited MPSprecision

1 true CPX_ON On: usefull-precision; default

See also

MPS file format: industry standard

network logging display switchDecides what CPLEX reports to the screen during network optimization.


Purpose

Network logging display switchC Name CPX_PARAM_NETDISPLAY (int)

C++ Name NetDisplay (int)

Java Name NetDisplay (int)

.NET Name NetDisplay (int)

OPL Name netdisplay


MTALAB Name not available in this API

Interactive Optimizer network display

Identifier 5005

Description

Decides what CPLEX reports to the screen during network optimization. Settings 1and 2 differ only during Phase I. Setting 2 shows monotonic values, whereas 1usually does not.


0 CPXNET_NO_DISPLAY_OBJECTIVE No display

1 CPXNET_TRUE_OBJECTIVE Display true objective values

2 CPXNET_PENALIZE_OBJECTIVE Display penalized objectivevalues; default

network optimality toleranceSpecifies the optimality tolerance for network optimization.

Purpose

Optimality tolerance for network optimizationC Name CPX_PARAM_NETEPOPT (double)

C++ Name NetEpOpt (double)

Java Name NetEpOpt (double)

.NET Name NetEpOpt (double)

OPL Name netepopt



Interactive Optimizer network tolerances optimality

Identifier 5002

Description

Specifies the optimality tolerance for network optimization; that is, the amount areduced cost may violate the criterion for an optimal solution.

Values



network primal feasibility toleranceSpecifies feasibility tolerance for network primal optimization. The feasibilitytolerance specifies the degree to which the flow value of a model may violate itsbounds.

Purpose

Feasibility tolerance for network primal optimizationC Name CPX_PARAM_NETEPRHS (double)

C++ Name NetEpRHS (double)

Java Name NetEpRHS (double)

.NET Name NetEpRHS (double)

OPL Name neteprhs



Interactive Optimizer network tolerances feasibility

Identifier 5003

Description

Specifies feasibility tolerance for network primal optimization. The feasibilitytolerance specifies the degree to which the flow value of a model may violate itsbounds. This tolerance influences the selection of an optimal basis and can be resetto a higher value when a problem is having difficulty maintaining feasibilityduring optimization. You may also wish to lower this tolerance after finding anoptimal solution if there is any doubt that the solution is truly optimal. If thefeasibility tolerance is set too low, CPLEX may falsely conclude that a problem isinfeasible. If you encounter reports of infeasibility during Phase II of theoptimization, a small adjustment in the feasibility tolerance may improveperformance.

Values


simplex network extraction levelEstablishes the level of network extraction for network simplex optimization.

Purpose

Simplex network extraction levelC Name CPX_PARAM_NETFIND (int)

C++ Name NetFind (int)

Java Name NetFind (int)

.NET Name NetFind (int)

OPL Name netfind



Interactive Optimizer network netfind

Identifier 1022


Description

Establishes the level of network extraction for network simplex optimization. Thedefault value is suitable for recognizing commonly used modeling approacheswhen representing a network problem within an LP formulation.

Table 40. Values


1 CPX_NETFIND_PURE Extract pure network only

2 CPX_NETFIND_REFLECT Try reflection scaling; default

3 CPX_NETFIND_SCALE Try general scaling

network simplex iteration limitSets the maximum number of iterations to be performed before the algorithmterminates without reaching optimality.

Purpose

Network simplex iteration limitC Name CPX_PARAM_NETITLIM (long)

C++ Name NetItLim (long)

Java Name NetItLim (long)

.NET Name NetItLim (long)

OPL Name netitlim



Interactive Optimizer network iterations

Identifier 5001

Description

Sets the maximum number of iterations to be performed before the algorithmterminates without reaching optimality.

Values


network simplex pricing algorithmSpecifies the pricing algorithm for network simplex optimization.

Purpose

Network simplex pricing algorithmC Name CPX_PARAM_NETPPRIIND (int)

C++ Name NetPPriInd (int)

Java Name NetPPriInd (int)

.NET Name NetPPriInd (int)

OPL Name netppriind




Interactive Optimizer network pricing

Identifier 5004

Description

Specifies the pricing algorithm for network simplex optimization. The default (0)shows best performance for most problems, and currently is equivalent to 3.

Table 41. Values


0 CPXNET_PRICE_AUTO Automatic: let CPLEX choose;default

1 CPXNET_PRICE_PARTIAL Partial pricing

2 CPXNET_PRICE_MULT_PART Multiple partial pricing

3 CPXNET_PRICE_SORT_MULT_PART Multiple partial pricing withsorting

MIP subproblem algorithmDecides which continuous optimizer will be used to solve the subproblems in aMIP, after the initial relaxation.

Purpose

MIP subproblem algorithmC Name CPX_PARAM_SUBALG (int)

C++ Name NodeAlg (int)

Java Name NodeAlg (int)

.NET Name NodeAlg (int)

OPL Name nodealg

Python Name mip.strategy.subalgorithm

MATLAB Name mip.strategy.subalgorithm

Interactive Optimizer mip strategy subalgorithm

Identifier 2026

Description

Decides which continuous optimizer will be used to solve the subproblems in aMIP, after the initial relaxation.

The default Automatic setting (0 zero) of this parameter currently selects the dualsimplex optimizer for subproblem solution for MILP and MIQP. The Automaticsetting may be expanded in the future so that CPLEX chooses the algorithm basedon additional characteristics of the model.

For MILP (integer constraints and otherwise continuous variable), all settings arepermitted.


For MIQP (integer constraints and positive semi-definite quadratic terms inobjective), setting 3 (Network) is not permitted, and setting 5 (Sifting) reverts to 0(Automatic).

For MIQCP (integer constraints and positive semi-definite quadratic terms amongthe constraints), only the Barrier optimizer is implemented, and therefore nosettings other than 0 (Automatic) and 4 (Barrier) are permitted.

Table 42. Values


0 CPX_ALG_AUTOMATIC Automatic: let CPLEX choose;default

1 CPX_ALG_PRIMAL Primal simplex

2 CPX_ALG_DUAL Dual simplex

3 CPX_ALG_NET Network simplex

4 CPX_ALG_BARRIER Barrier

5 CPX_ALG_SIFTING Sifting

node storage file switchUsed when working memory (CPX_PARAM_WORKMEM, WorkMem) has been exceeded bythe size of the tree.

Purpose

Node storage file switchC Name CPX_PARAM_NODEFILEIND (int)

C++ Name NodeFileInd (int)

Java Name NodeFileInd (int)

.NET Name NodeFileInd (int)

OPL Name nodefileind

Python Name mip.strategy.file

MATLAB Name mip.strategy.file

Interactive Optimizer mip strategy file

Identifier 2016

Description

Used when working memory (CPX_PARAM_WORKMEM, WorkMem) has been exceeded bythe size of the tree. If the node file parameter is set to zero when the tree memorylimit is reached, optimization is terminated. Otherwise, a group of nodes isremoved from the in-memory set as needed. By default, CPLEX transfers nodes tonode files when the in-memory set is larger than 128 MBytes, and it keeps theresulting node files in compressed form in memory. At settings 2 and 3, the nodefiles are transferred to disk, in uncompressed and compressed form respectively,into a directory named by the working directory parameter (CPX_PARAM_WORKDIR,WorkDir), and CPLEX actively manages which nodes remain in memory forprocessing.

Value Meaning

0 No node file


Value Meaning

1 Node file in memory and compressed;default

2 Node file on disk

3 Node file on disk and compressed

Related reference:“directory for working files” on page 140Specifies the name of an existing directory into which CPLEX may store temporaryworking files.“memory available for working storage” on page 141Specifies an upper limit on the amount of central memory, in megabytes, thatCPLEX is permitted to use for working memory.

MIP node limitSets the maximum number of nodes solved before the algorithm terminateswithout reaching optimality.

Purpose

MIP node limitC Name CPX_PARAM_NODELIM (long)

C++ Name NodeLim (long)

Java Name NodeLim (long)

.NET Name NodeLim (long)

OPL Name nodelim

Python Name mip.limits.nodes

MATLAB Name mip.limits.nodes in Cplex Class API

MATLAB Name mip.limits.nodes in Toolbox (CPLEX compatible)

MATLAB Name MaxNodes in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer mip limits nodes

Identifier 2017

Description

Sets the maximum number of nodes solved before the algorithm terminateswithout reaching optimality. When this parameter is set to 0 (zero), CPLEXcompletes processing at the root; that is, it creates cuts and applies heuristics at theroot. When this parameter is set to 1 (one), it allows branching from the root; thatis, nodes are created but not solved.

Values


MIP node selection strategyUsed to set the rule for selecting the next node to process when backtracking.


Purpose

MIP node selection strategyC Name CPX_PARAM_NODESEL (int)

C++ Name NodeSel (int)

Java Name NodeSel (int)

.NET Name NodeSel (int)

OPL Name nodesel

Python Name mip.strategy.nodeselect

MATLAB Name mip.strategy.nodeselect in Cplex Class API

MATLAB Name mip.strategy.nodeselect in Toolbox (CPLEX compatible)

MATLAB Name NodeSearchStrategy in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer mip strategy nodeselect

Identifier 2018

Description

Sets the rule for selecting the next node to process when the search is backtracking.The depth-first search strategy chooses the most recently created node. Thebest-bound strategy chooses the node with the best objective function for theassociated LP relaxation. The best-estimate strategy selects the node with the bestestimate of the integer objective value that would be obtained from a node once allinteger infeasibilities are removed. An alternative best-estimate search is alsoavailable.

Table 43. Values


0 CPX_NODESEL_DFS Depth-first search

1 CPX_NODESEL_BESTBOUND Best-bound search; default

2 CPX_NODESEL_BESTEST Best-estimate search

3 CPX_NODESEL_BESTEST_ALT Alternative best-estimatesearch

numerical precision emphasisEmphasizes precision in numerically unstable or difficult problems.

Purpose

Numerical precision emphasisC Name CPX_PARAM_NUMERICALEMPHASIS (int)

C++ Name NumericalEmphasis (bool)

Java Name NumericalEmphasis (bool)

.NET Name NumericalEmphasis (bool)

OPL Name numericalemphasis

Python Name emphasis.numerical

MATLAB Name emphasis.numerical

Interactive Optimizer emphasis numerical

Identifier 1083


Description

Emphasizes precision in numerically unstable or difficult problems. This parameterlets you specify to CPLEX that it should emphasize precision in numericallydifficult or unstable problems, with consequent performance trade-offs in time andmemory.

Table 44. Values


0 false CPX_OFF Do not emphasizenumerical precision;default

1 true CPX_ON Exercise extremecaution incomputation

nonzero element read limitSpecifies a limit for the number of nonzero elements to read for an allocation ofmemory.

Purpose

Nonzero element read limitC Name 32-bit API CPX_PARAM_NZREADLIM (CPXINT)

C Name 64-bit API CPX_PARAM_NZREADLIM (CPXLONG)

C++ Name NzReadLim (CPXLONG)

Java Name NzReadLim (CPXLONG)

.NET Name NzReadLim (CPXLONG)

Python Name read.nonzeros

MATLAB Name read.nonzeros

Interactive Optimizer read nonzeros

Identifier 1024

Description

Specifies a limit for the number of nonzero elements to read for an allocation ofmemory. This parameter does not restrict the size of a problem. Rather, it indirectlyspecifies the default amount of memory that will be pre-allocated before a problemis read from a file. If the limit is exceeded, more memory is automaticallyallocated.

Values

Any integer from 0 to CPX_BIGINT or CPX_BIGLONG, depending on integer type;default: 250 000.

absolute objective difference cutoffUsed to update the cutoff each time a mixed integer solution is found.


Purpose

Absolute objective difference cutoffC Name CPX_PARAM_OBJDIF (double)

C++ Name ObjDif (double)

Java Name ObjDif (double)

.NET Name ObjDif (double)

OPL Name objdif

Python Name mip.tolerances.objdifference

MATLAB Name mip.tolerances.objdifference

Interactive Optimizer mip tolerances objdifference

Identifier 2019

Description

Used to update the cutoff each time a mixed integer solution is found. Thisabsolute value is subtracted from (added to) the newly found integer objectivevalue when minimizing (maximizing). This forces the mixed integer optimizationto ignore integer solutions that are not at least this amount better than the best onefound so far.

The objective difference parameter can be adjusted to improve problem solvingefficiency by limiting the number of nodes; however, setting this parameter at avalue other than zero (the default) can cause some integer solutions, including thetrue integer optimum, to be missed.

Negative values for this parameter can result in some integer solutions that areworse than or the same as those previously generated, but does not necessarilyresult in the generation of all possible integer solutions.

Values

Any number; default: 0.0.Related reference:“relative objective difference cutoff” on page 110Used to update the cutoff each time a mixed integer solution is found.

lower objective value limitSets a lower limit on the value of the objective function in the simplex algorithms.

Purpose

Lower objective value limitC Name CPX_PARAM_OBJLLIM (double)

C++ Name ObjLLim (double)

Java Name ObjLLim (double)

.NET Name ObjLLim (double)

OPL Name objllim

Python Name simplex.limits.lowerobj

MATLAB Name simplex.limits.lowerobj

Interactive Optimizer simplex limits lowerobj


Identifier 1025

Description

Sets a lower limit on the value of the objective function in the simplex algorithms.Setting a lower objective function limit causes CPLEX to halt the optimizationprocess when the minimum objective function value limit has been reached. Thislimit applies only during Phase II of the simplex algorithm in minimizationproblems.

Tip:

This parameter is not effective with the conflict refiner nor with FeasOpt. That is,neither of those tools can analyze an infeasibility introduced by this parameter. Ifyou want to analyze such a condition, add an explicit objective constraint, such asobj >= c, to your model instead before you invoke either of those tools.

Values

Any number; default: -1e+75.

upper objective value limitSets an upper limit on the value of the objective function in the simplexalgorithms.

Purpose

Upper objective value limitC Name CPX_PARAM_OBJULIM (double)

C++ Name ObjULim (double)

Java Name ObjULim (double)

.NET Name ObjULim (double)

OPL Name objulim

Python Name simplex.limits.upperobj

MATLAB Name simplex.limits.upperobj

Interactive Optimizer simplex limits upperobj

Identifier 1026

Description

Sets an upper limit on the value of the objective function in the simplexalgorithms. Setting an upper objective function limit causes CPLEX to halt theoptimization process when the maximum objective function value limit has beenreached. This limit applies only during Phase II of the simplex algorithm inmaximization problems.

Tip:

This parameter is not effective with the conflict refiner nor with FeasOpt. That is,neither of those tools can analyze an infeasibility introduced by this parameter. Ifyou want to analyze such a condition, add an explicit objective constraint, such asobj <= c. to your model instead before you invoke either of those tools.


Values

Any number; default: 1e+75.

parallel mode switchSets the parallel optimization mode. Possible modes are automatic, deterministic,and opportunistic.

Purpose

Parallel mode switchC Name CPX_PARAM_PARALLELMODE (int)

C++ Name ParallelMode (int)

Java Name ParallelMode (int)

.NET Name ParallelMode (int)

OPL Name parallelmode

Python Name parallel

MATLAB Name parallel

Interactive Optimizer parallel

Identifier 1109

Description

Sets the parallel optimization mode. Possible modes are automatic, deterministic,and opportunistic.

In this context, deterministic means that multiple runs with the same model at thesame parameter settings on the same platform will reproduce the same solutionpath and results. In contrast, opportunistic implies that even slight differences intiming among threads or in the order in which tasks are executed in differentthreads may produce a different solution path and consequently different timingsor different solution vectors during optimization executed in parallel threads. Inmultithreaded applications, the opportunistic setting entails less synchronizationbetween threads and consequently may provide better performance.

By default, CPLEX applies as much parallelism as possible while still achievingdeterministic results. That is, when you run the same model twice on the sameplatform with the same parameter settings, you will see the same solution andoptimization run. This condition is referred to as the deterministic mode.

More opportunities to exploit parallelism are available if you do not requiredeterminism. In other words, CPLEX can find more possibilities for parallelism ifyou do not require an invariant, repeatable solution path and precisely the samesolution vector. To use all available parallelism, you need to select the opportunisticparallel mode. In this mode, CPLEX will use all opportunities for parallelism inorder to achieve best performance.

However, in opportunistic mode, the actual optimization may differ from run torun, including the solution time itself and the path traveled in the search.

Deterministic and sequential optimization

Parallel MIP optimization can be opportunistic or deterministic.


Parallel barrier optimization is only deterministic.

Concurrent optimization can be opportunistic or deterministic. In opportunisticmode, when multiple threads are available, concurrent optimization launchesprimal simplex, dual simplex, and barrier optimizers by default. In deterministicmode, when multiple threads are available, concurrent optimization launches thedual simplex and barrier optimizers by default.

Callbacks and MIP optimization

If callbacks other than informational callbacks are used for solving a MIP, the orderin which the callbacks are called cannot be guaranteed to remain deterministic, noteven in deterministic mode. Thus, to make sure of deterministic runs when theparallel mode parameter and the global default thread count parameter are at theirdefault settings, CPLEX reverts to sequential solving of the MIP in the presence ofquery callbacks, diagnostic callbacks, or control callbacks.

Consequently, if your application invokes query, diagnostic, or control callbacks,and you still prefer deterministic parallel search, you can choose value 1 (one),overriding the automatic setting and turning on deterministic parallel search. It isthen your responsibility to make sure that your callbacks do not performoperations that could lead to opportunistic behavior and are implemented in athread-safe way. To meet these conditions, your application must not store andmust not update any information in the callbacks.

Determinism versus opportunism

This parameter also allows you to turn off this default setting by choosing value -1(minus one). Cases where you might wish to turn off deterministic search includesituations where you want to take advantage of possibly faster performance ofopportunistic parallel MIP optimization in multiple threads after you haveconfirmed that deterministic parallel MIP optimization produced the results youexpected.

Table 45. Values

Value Symbolic Constant

Callable Library

Symbolic Constant

Concert Technology

Meaning

-1 CPX_PARALLEL_OPPORTUNISTICOpportunistic Enable opportunisticparallel search mode

0 CPX_PARALLEL_AUTO AutoParallel Automatic: let CPLEXdecide whether toinvoke deterministic oropportunistic search;default

1 CPX_PARALLEL_DETERMINISTICDeterministic Enable deterministicparallel search mode

See also: “global default thread count” on page 131: CPX_PARAM_THREADS, Threads

simplex perturbation switchDecides whether to perturb problems.


Purpose

Simplex perturbation switchC Name CPX_PARAM_PERIND (int)

C++ Name PerInd (bool)

Java Name PerInd (bool)

.NET Name PerInd (bool)

OPL Name perind

Python Name simplex.perturbation

MATLAB Name simplex.perturbation

Interactive Optimizer simplex perturbation

Identifier 1027

Description

Decides whether to perturb problems.

Setting this parameter to 1 (one) causes all problems to be automatically perturbedas optimization begins. A setting of 0 (zero) allows CPLEX to decide dynamically,during solution, whether progress is slow enough to merit a perturbation. Thesituations in which a setting of 1 (one) helps are rare and restricted to problemsthat exhibit extreme degeneracy.

Table 46. Values


0 false CPX_OFF Automatic: letCPLEX choose;default

1 true CPX_ON Turn on perturbationfrom beginning

simplex perturbation limitSets the number of degenerate iterations before perturbation is performed.

Purpose

Simplex perturbation limitC Name CPX_PARAM_PERLIM (int)

C++ Name PerLim (int)

Java Name PerLim (int)

.NET Name PerLim (int)

OPL Name perlim

Python Name simplex.limits.perturbation

MATLAB Name simplex.limits.perturbation

Interactive Optimizer simplex limits perturbation

Identifier 1028


Description

Sets the number of degenerate iterations before perturbation is performed.

Table 47. Values

Value Meaning


Any positive integer Number of degenerate iterations beforeperturbation

deterministic time before starting to polish a feasible solutionSets the amount of time expressed in deterministic ticks to spend during a normalmixed integer optimization after which CPLEX starts to polish a feasible solution

Purpose

Deterministic time before starting to polish a feasible solutionC Name CPX_PARAM_POLISHAFTERDETTIME (double)

C++ Name PolishAfterDetTime (double)

Java Name PolishAfterDetTime (double)

.NET Name PolishAfterTime (double)

OPL Name polishafterdettime

Python Name mip.polishing.dettime

MATLAB Name mip.polishafter.dettime

Interactive Optimizer mip polishafter dettime

Identifier 2151

Description

Tells CPLEX how much time (expressed in deterministic ticks) to spend duringmixed integer optimization before CPLEX starts polishing a feasible solution. Thedefault value (1.0E+75 seconds) is such that CPLEX does not start solutionpolishing by default.

Starting conditions

CPLEX must have a feasible solution in order to start polishing. It must also havecertain internal structures in place to support solution polishing. Consequently,when the criterion specified by this parameter is met, CPLEX begins solutionpolishing only after these starting conditions are also met. That is, there may be adelay between the moment when the criterion specified by this parameter is metand when solution polishing starts.

Values

Any nonnegative value in deterministic ticks; default:1.0E+75 ticks.

See also

“time before starting to polish a feasible solution” on page 97


absolute MIP gap before starting to polish a feasible solutionSets an absolute MIP gap after which CPLEX starts to polish a feasible solution

Purpose

Absolute MIP gap before starting to polish a feasible solutionC Name CPX_PARAM_POLISHAFTEREPAGAP (double)

C++ Name PolishAfterEpAGap (double)

Java Name PolishAfterEpAGap (double)

.NET Name PolishAfterEpAGap (double)

OPL Name polishafterepagap

Python Name mip.polishing.absmipgap

MATLAB Name mip.polishing.absmipgap

Interactive Optimizer mip polishafter absmipgap

Identifier 2126

Description

Sets an absolute MIP gap (that is, the difference between the best integer objectiveand the objective of the best node remaining) after which CPLEX stopsbranch-and-cut and begins polishing a feasible solution. The default value (0.0) issuch that CPLEX does not invoke solution polishing by default.

Starting conditions


Values

Any nonnegative value; default: 0.0.

See also


relative MIP gap before starting to polish a feasible solutionSets a relative MIP gap after which CPLEX starts to polish a feasible solution

Purpose

Relative MIP gap before starting to polish a solutionC Name CPX_PARAM_POLISHAFTEREPGAP (double)

C++ Name PolishAfterEpGap (double)

Java Name PolishAfterEpGap (double)

.NET Name PolishAfterEpGap (double)

OPL Name polishafterepgap


Python Name mip.polishing.mipgap

MATLAB Name mip.polishing.mipgap

Interactive Optimizer mip polishafter mipgap

Identifier 2127

Description

Sets a relative MIP gap after which CPLEX will stop branch-and-cut and beginpolishing a feasible solution. The default value (0.0) is such that CPLEX does notinvoke solution polishing by default. The relative MIP gap is calculated like this:

|bestbound-bestinteger|/(1e-10+|bestinteger|)

Starting conditions


Values

Any number from 0.0 to 1.0, inclusive; default: 0.0.

See also


MIP integer solutions to find before starting to polish a feasiblesolution

Sets the number of integer solutions to find after which CPLEX starts to polish afeasible solution

Purpose

MIP integer solutions to find before starting to polish a feasible solutionC Name CPX_PARAM_POLISHAFTERINTSOL (long)

C++ Name PolishAfterIntSol (long)

Java Name PolishAfterIntSol (long)

.NET Name PolishAfterIntSol (long)

OPL Name polishafterintsol

Python Name mip.polishing.solutions

MATLAB Name mip.polishing.solutions

Interactive Optimizer mip polishafter solutions

Identifier 2129

Description

Sets the number of integer solutions to find before CPLEX stops branch-and-cutand begins to polish a feasible solution. The default value is such that CPLEX doesnot invoke solution polishing by default.


Starting conditions


Values

Any positive integer strictly greater than zero; zero is not allowed; default:9223372036800000000

See also

“MIP integer solution-file switch and prefix” on page 63

nodes to process before starting to polish a feasible solutionSets the number of nodes to process after which CPLEX starts to polish a feasiblesolution

Purpose

Nodes to process before starting to polish a feasible solutionC Name CPX_PARAM_POLISHAFTERNODE (long)

C++ Name PolishAfterNode (long)

Java Name PolishAfterNode (long)

.NET Name PolishAfterNode (long)

OPL Name polishafternode

Python Name mip.polishing.nodes

MATLAB Name mip.polishing.nodes

Interactive Optimizer mip polishafter nodes

Identifier 2128

Description

Sets the number of nodes processed in branch-and-cut before CPLEX startssolution polishing, if a feasible solution is available.

When this parameter is set to 0 (zero), CPLEX completes processing at the root;that is, it creates cuts and applies heuristics at the root.

When this parameter is set to 1 (one), it allows branching from the root; that is,nodes are created but not solved.

When no feasible solution is available yet, CPLEX explores more nodes than thenumber specified by this parameter.

Starting conditions

CPLEX must have a feasible solution in order to start polishing. It must also havecertain internal structures in place to support solution polishing. Consequently,


when the criterion specified by this parameter is met, CPLEX begins solutionpolishing only after these starting conditions are also met. That is, there may be adelay between the moment when the criterion specified by this parameter is metand when solution polishing starts.

Values


See also

“MIP node limit” on page 85

time before starting to polish a feasible solutionSets the amount of time in seconds to spend during a normal mixed integeroptimization after which CPLEX starts to polish a feasible solution

Purpose

Time before starting to polish a feasible solutionC Name CPX_PARAM_POLISHAFTERTIME (double)

C++ Name PolishAfterTime (double)

Java Name PolishAfterTime (double)

.NET Name PolishAfterTime (double)

OPL Name polishaftertime

Python Name mip.polishing.time

MATLAB Name mip.polishafter.time

Interactive Optimizer mip polishafter time

Identifier 2130

Description

Tells CPLEX how much time in seconds to spend during mixed integeroptimization before CPLEX starts polishing a feasible solution. The default value(1.0E+75 seconds) is such that CPLEX does not start solution polishing by default.

Whether CPLEX measures CPU time or wall clock time (also known as real time)depends on the parameter “clock type for computation time” on page 35.

Starting conditions


Values

Any nonnegative value in seconds; default:1.0E+75 seconds.

See also



“deterministic time before starting to polish a feasible solution” on page 93

time spent polishing a solution (deprecated)Deprecated parameter

Purpose

Time spent polishing a solution (deprecated)C Name CPX_PARAM_POLISHTIME (double)

C++ Name PolishTime (double)

Java Name PolishTime (double)

.NET Name PolishTime (double)

OPL Name polishtime

Python Name deprecated and not available

MATLAB Name deprecated and not available

Interactive Optimizer mip limit polishtime

Identifier 2066

Description

This deprecated parameter told CPLEX how much time in seconds to spend after anormal mixed integer optimization in polishing a solution. The default was zero,no polishing time.

Instead of this deprecated parameter, use one of the following parameters tocontrol the effort that CPLEX spends in branch-and-cut before it begins polishing afeasible solution:v “absolute MIP gap before starting to polish a feasible solution” on page 94v “relative MIP gap before starting to polish a feasible solution” on page 94v “MIP integer solutions to find before starting to polish a feasible solution” on

page 95v “nodes to process before starting to polish a feasible solution” on page 96v “time before starting to polish a feasible solution” on page 97v “optimizer time limit in seconds” on page 132

Values

Any nonnegative value in seconds; default: 0.0 (zero) seconds.

maximum number of solutions generated for solution pool by populateSets the maximum number of mixed integer programming (MIP) solutionsgenerated for the solution pool during each call to the populate procedure.

Purpose

Maximum number of solutions generated for the solution pool by populateC Name CPX_PARAM_POPULATELIM (int)

C++ Name PopulateLim (int)


Java Name PopulateLim (int)

.NET Name PopulateLim (int)

OPL Name populatelim

Python Name mip.limits.populate

MATLAB Name mip.limits.populate

Interactive Optimizer mip limits populate

Identifier 2108

Description

Sets the maximum number of mixed integer programming (MIP) solutionsgenerated for the solution pool during each call to the populate procedure.Populate stops when it has generated PopulateLim solutions. A solution is countedif it is valid for all filters, consistent with the relative and absolute pool gapparameters, and has not been rejected by the incumbent callback (if any exists),whether or not it improves the objective of the model.

In parallel, populate may not respect this parameter exactly due to disparitiesbetween threads. That is, it may happen that populate stops when it has generateda number of solutions slightly more than or slightly less than this limit because ofdifferences in synchronization between threads.

This parameter does not apply to MIP optimization generally; it applies only to thepopulate procedure.

If you are looking for a parameter to control the number of solutions stored in thesolution pool, consider instead the solution pool capacity parameter (“maximumnumber of solutions kept in solution pool” on page 123: SolnPoolCapacity,CPX_PARAM_SOLNPOOLCAPACITY).

Populate will stop before it reaches the limit set by this parameter if it reachesanother limit, such as a time limit set by the user. Additional stopping criteria canbe specified by these parameters:v “relative gap for solution pool” on page 124: SolnPoolGap,

CPX_PARAM_SOLNPOOLGAP

v “absolute gap for solution pool” on page 122: SolnPoolAGap,CPX_PARAM_SOLNPOOLAGAP

v “MIP node limit” on page 85: NodeLim, CPX_PARAM_NODELIMv “optimizer time limit in seconds” on page 132: TiLim, CPX_PARAM_TILIM

Values


primal simplex pricing algorithmSets the primal simplex pricing algorithm.

Purpose

Primal simplex pricing algorithmC Name CPX_PARAM_PPRIIND (int)

C++ Name PPriInd (int)


Java Name PPriInd (int)

.NET Name PPriInd (int)

OPL Name ppriind

Python Name simplex.pgradient

MATLAB Name simplex.pgradient

Interactive Optimizer simplex pgradient

Identifier 1029

Description

Sets the primal simplex pricing algorithm. The default pricing (0) usually providesthe fastest solution time, but many problems benefit from alternative settings.

Table 48. Values


-1 CPX_PPRIIND_PARTIAL Reduced-cost pricing

0 CPX_PPRIIND_AUTO Hybrid reduced-cost & devex pricing; default

1 CPX_PPRIIND_DEVEX Devex pricing

2 CPX_PPRIIND_STEEP Steepest-edge pricing

3 CPX_PPRIIND_STEEPQSTART Steepest-edge pricing with slack initial norms

4 CPX_PPRIIND_FULL Full pricing

presolve dual settingDecides whether CPLEX presolve should pass the primal or dual linearprogramming problem to the linear programming optimization algorithm.

Purpose

Presolve dual settingC Name CPX_PARAM_PREDUAL (int)

C++ Name PreDual (int)

Java Name PreDual (int)

.NET Name PreDual (int)

OPL Name predual

Python Name preprocessing.dual

MATLAB Name preprocessing.dual

Interactive Optimizer preprocessing dual

Identifier 1044

Description

Decides whether CPLEX presolve should pass the primal or dual linearprogramming problem to the linear programming optimization algorithm. Bydefault, CPLEX chooses automatically.

If this parameter is set to 1 (one), the CPLEX presolve algorithm is applied to theprimal problem, but the resulting dual linear program is passed to the optimizer.This is a useful technique for problems with more constraints than variables.


When this parameter is set to 0 (zero, its default value) or 1 (one, turned on),CPLEX disables crushing and uncrushing of the model by such routines asCPXuncrushx. To use CPXuncrushx effectively, you must set the value of thisparameter to -1, turning off this feature.

Table 49. Values

Value Meaning

-1 Turn off this feature


1 Turn on this feature

presolve switchDecides whether CPLEX applies presolve during preprocessing.

Purpose

Presolve switchC Name CPX_PARAM_PREIND (int)

C++ Name PreInd (bool)

Java Name PreInd (bool)

.NET Name PreInd (bool)

OPL Name preind

Python Name preprocessing.presolve

MATLAB Name preprocessing.presolve

Interactive Optimizer preprocessing presolve

Identifier 1030

Description

Decides whether CPLEX applies presolve during preprocessing. When set to 1(one), the default, this parameter invokes CPLEX presolve to simplify and reduceproblems. In other words, this parameter turns on or off presolve duringpreprocessing.

To limit the number of passes that CPLEX carries out in presolve, use anotherparameter: “limit on the number of presolve passes made” on page 102(CPX_PARAM_PREPASS, PrePass).

Table 50. Values


0 false CPX_OFF Do not applypresolve

1 true CPX_ON Apply presolve;default

linear reduction switchDecides whether linear or full reductions occur during preprocessing.


Purpose

Linear reduction switchC Name CPX_PARAM_PRELINEAR (int)

C++ Name PreLinear (int)

Java Name PreLinear (int)

.NET Name PreLinear (int)

OPL Name prelinear

Python Name preprocessing.linear

MATLAB Name preprocessing.linear

Interactive Optimizer preprocessing linear

Identifier 1058

Description

Decides whether linear or full reductions occur during preprocessing. If only linearreductions are performed, each variable in the original model can be expressed asa linear form of variables in the presolved model. This condition guarantees, forexample, that users can add their own custom cuts to the presolved model.

If your application uses lazy constraints (for example, you have explicitly addedlazy constraints to the model before optimization, or you have registered lazyconstraints from a callback by means of a method or routine, such asCPXsetlazyconstraintcallbackfunc) then CPLEX turns off nonlinear reductions.

Table 51. Values

Value Meaning

0 Perform only linear reductions

1 Perform full reductions; default

limit on the number of presolve passes madeLimits the number of presolve passes that CPLEX makes during preprocessing.

Purpose

Limit on the number of presolve passes during preprocessingC Name CPX_PARAM_PREPASS (int)

C++ Name PrePass (int)

Java Name PrePass (int)

.NET Name PrePass (int)

OPL Name prepass

Python Name preprocessing.numpass

MATLAB Name preprocessing.numpass

Interactive Optimizer preprocessing numpass

Identifier 1052

Description

Limits the number of presolve passes that CPLEX makes during preprocessing.


When this parameter is set to a positive value, presolve is applied the specifiednumber of times, or until no more reductions are possible.

At the default value of -1, presolve continues only if it seems to be helping.

When this parameter is set to zero, CPLEX does not enter its main presolve loop,but other reductions may occur, depending on settings of other parameters andcharacteristics of your model. In other words, setting this parameter to 0 (zero) isnot equivalent to turning off the “presolve switch” on page 101(CPX_PARAM_PREIND, PreInd). To turn off presolve, use the “presolve switch”on page 101 (CPX_PARAM_PREIND, PreInd) instead.

Table 52. Values

Value Meaning

-1 Automatic: let CPLEX choose; presolvecontinues as long as helpful; default

0 Do not use presolve; other reductions maystill occur

Any positive integer Apply presolve specified number of times

node presolve switchDecides whether node presolve should be performed at the nodes of a mixedinteger programming (MIP) solution.

Purpose

Node presolve switchC Name CPX_PARAM_PRESLVND (int)

C++ Name PreslvNd (int)

Java Name PreslvNd (int)

.NET Name PreslvNd (int)

OPL Name preslvnd

Python Name mip.strategy.presolvenode

MATLAB Name mip.strategy.presolvenode

Interactive Optimizer mip strategy presolvenode

Identifier 2037

Description

Decides whether node presolve should be performed at the nodes of a mixedinteger programming (MIP) solution. Node presolve can significantly reducesolution time for some models. The default setting is generally effective at decidingwhether to apply node presolve, although runtimes can be reduced for somemodels by the user turning node presolve off.

Value Meaning

-1 No node presolve


1 Force presolve at nodes

2 Perform probing on integer-infeasible variables


Value Meaning

3 Perform aggressive node probing

simplex pricing candidate list sizeSets the maximum number of variables kept in the list of pricing candidates for thesimplex algorithms.

Purpose

Simplex pricing candidate list sizeC Name CPX_PARAM_PRICELIM (int)

C++ Name PriceLim (int)

Java Name PriceLim (int)

.NET Name PriceLim (int)

OPL Name pricelim

Python Name simplex.pricing

MATLAB Name simplex.pricing

Interactive Optimizer simplex pricing

Identifier 1010

Description

Sets the maximum number of variables kept in the list of pricing candidates for thesimplex algorithms.

Table 53. Values

Value Meaning


Any positive integer Number of pricing candidates

MIP probing levelSets the amount of probing on variables to be performed before MIP branching.

Purpose

MIP probing levelC Name CPX_PARAM_PROBE (int)

C++ Name Probe (int)

Java Name Probe (int)

.NET Name Probe (int)

OPL Name probe

Python Name mip.strategy.probe

MATLAB Name mip.strategy.probe

Interactive Optimizer mip strategy probe

Identifier 2042


Description

Sets the amount of probing on variables to be performed before MIP branching.Higher settings perform more probing. Probing can be very powerful but verytime-consuming at the start. Setting the parameter to values above the default of 0(automatic) can result in dramatic reductions or dramatic increases in solutiontime, depending on the model.

Table 54. Values

Value Meaning

-1 No probing


1 Moderate probing level

2 Aggressive probing level

3 Very aggressive probing level

deterministic time spent probingLimits the amount of time (expressed in deterministic ticks) spent probing.

Purpose

Time spent probing, measured deterministicallyC Name CPX_PARAM_PROBEDETTIME (double)

C++ Name ProbeDetTime (double)

Java Name ProbeDetTime (double)

.NET Name ProbeDetTime (double)

OPL Name probedettime

Python Name mip.limits.probedettime

MATLAB Name mip.limits.probedettime

Interactive Optimizer mip limits probedettime

Identifier 2150

Description

Limits the amount of time (expressed in deterministic ticks) spent probing.

For a parameter limiting the amount of time spent probing in seconds, (rather thandeterministic ticks) see “time spent probing” (CPX_PARAM_PROBETIME,ProbeTime).

Values

Any nonnegative number; default: 1e+75.

time spent probingLimits the amount of time in seconds spent probing.


Purpose

Time spent probingC Name CPX_PARAM_PROBETIME (double)

C++ Name ProbeTime (double)

Java Name ProbeTime (double)

.NET Name ProbeTime (double)

OPL Name probetime

Python Name mip.limits.probetime

MATLAB Name mip.limits.probetime

Interactive Optimizer mip limits probetime

Identifier 2065

Description

Limits the amount of time in seconds spent probing.

For a parameter limiting the amount of time spent probing in deterministic ticks(rather than seconds) see “deterministic time spent probing” on page 105(CPX_PARAM_PROBEDETTIME, ProbeDetTime).

Values


indefinite MIQP switchDecides whether CPLEX will attempt to reformulate a MIQP or MIQCP model thatcontains only binary variables.

Purpose

Indefinite MIQP switchC Name CPX_PARAM_QPMAKEPSDIND (int)

C++ Name QPmakePSDInd (bool)

Java Name QPmakePSDInd (bool)

.NET Name QPmakePSDInd (bool)

OPL Name qpmakepsdind

Python Name preprocessing.qpmakepsd

MATLAB Name preprocessing.qpmakepsd

Interactive Optimizer preprocessing qpmakepsd

Identifier 4010

Description

Decides whether CPLEX will attempt to reformulate a MIQP or MIQCP model thatcontains only binary variables. When this feature is active, adjustments will bemade to the elements of a quadratic matrix that is not nominally positivesemi-definite (PSD, as required by CPLEX for all QP and most QCP formulations),to make it PSD, and CPLEX will also attempt to tighten an already PSD matrix forbetter numerical behavior. The default setting of 1 (one) means yes, CPLEX shouldattempt to reformulate, but you can turn it off if necessary; most models benefitfrom the default setting.


Table 55. Values


0 false CPX_OFF Turn off attempts tomake binary modelPSD

1 true CPX_ON On: CPLEX attemptsto make binarymodel PSD; default

QP Q-matrix nonzero read limitSpecifies a limit for the number of nonzero elements to read for an allocation ofmemory in a model with a quadratic matrix.

Purpose

QP Q-matrix nonzero read limitC Name 32-bit API CPX_PARAM_QPNZREADLIM (CPXINT)

C Name 64-bit API CPX_PARAM_QPNZREADLIM (CPXLONG)

C++ Name QPNzReadLim (CPXLONG)

Java Name QPNzReadLim (CPXLONG)

.NET Name QPNzReadLim (CPXLONG)

Python Name read.qpnonzeros

MATLAB Name read.qpnonzeros

Interactive Optimizer read qpnonzeros

Identifier 4001

Description

Specifies a limit for the number of nonzero elements to read for an allocation ofmemory in a model with a quadratic matrix.


Values

Any integer from 0 (zero) to CPX_BIGINT or CPX_BIGLONG, depending on the type ofinteger; default: 5 000.

random seedThis parameter sets the random seed differently for diversity of solutions.

Purpose

Set random seed differently for diversity of solutions.C Name CPX_PARAM_RANDOMSEED (int)

C++ Name RandomSeed (int)

Java Name RandomSeed (int)

.NET Name RandomSeed (int)


OPL Name randomseed

Python Name random_seed

MATLAB Name randomseed

InteractiveOptimizer randomseed

Identifier 1124

Description

This parameter makes it possible for your application to manage the random seedthat CPLEX uses in some of its internal operations. Variation in the random seedcan increase diversity of results.

Values

Any nonnegative integer; that is, an integer in the interval [0, BIGINT].

The default value of this parameter changes with each release.

primal and dual reduction typeSpecifies whether primal reductions, dual reductions, both, or neither areperformed during preprocessing.

Purpose

Primal and dual reduction typeC Name CPX_PARAM_REDUCE (int)

C++ Name Reduce (int)

Java Name Reduce (int)

.NET Name Reduce (int)

OPL Name reduce

Python Name preprocessing.reduce

MATLAB Name preprocessing.reduce

Interactive Optimizer preprocessing reduce

Identifier 1057

Description

Specifies whether primal reductions, dual reductions, both, or neither areperformed during preprocessing. These preprocessing reductions are also known aspresolve reductions.

If your application uses lazy constraints (for example, you have explicitly addedlazy constraints to the model before optimization, or you have registered lazyconstraints from a callback by means of a method or routine, such asCPXsetlazyconstraintcallbackfunc) then CPLEX turns off dual reductions.

Table 56. Values


0 CPX_PREREDUCE_NOPRIMALORDUAL No primal or dual reductions

1 CPX_PREREDUCE_PRIMALONLY Only primal reductions

2 CPX_PREREDUCE_DUALONLY Only dual reductions




3 CPX_PREREDUCE_PRIMALANDDUAL Both primal and dual reductions;default

simplex refactoring frequencySets the number of iterations between refactoring of the basis matrix.

Purpose

Simplex refactoring frequencyC Name CPX_PARAM_REINV (int)

C++ Name ReInv (int)

Java Name ReInv (int)

.NET Name ReInv (int)

OPL Name reinv

Python Name simplex.refactor

MATLAB Name simplex.refactor

Interactive Optimizer simplex refactor

Identifier 1031

Description

Sets the number of iterations between refactoring of the basis matrix.

Table 57. Values

Value Meaning


Integer from 1 to 10 000 Number of iterations between refactoring ofthe basis matrix

relaxed LP presolve switchDecides whether LP presolve is applied to the root relaxation in a mixed integerprogram (MIP).

Purpose

Relaxed LP presolve switchC Name CPX_PARAM_RELAXPREIND (int)

C++ Name RelaxPreInd (int)

Java Name RelaxPreInd (int)

.NET Name RelaxPreInd (int)

OPL Name relaxpreind

Python Name preprocessing.relax

MATLAB Name preprocessing.relax

Interactive Optimizer preprocessing relax

Identifier 2034


Description

Decides whether LP presolve is applied to the root relaxation in a mixed integerprogram (MIP). Sometimes additional reductions can be made beyond any MIPpresolve reductions that were already done. By default, CPLEX applies presolve tothe initial relaxation in order to hasten time to the initial solution.


-1 Automatic: let CPLEXchoose; default

0 CPX_OFF Off: do not use presolve oninitial relaxation

1 CPX_ON On: use presolve on initialrelaxation

relative objective difference cutoffUsed to update the cutoff each time a mixed integer solution is found.

Purpose

Relative objective difference cutoffC Name CPX_PARAM_RELOBJDIF (double)

C++ Name RelObjDif (double)

Java Name RelObjDif (double)

.NET Name RelObjDif (double)

OPL Name relobjdif

Python Name mip.tolerances.relobjdifference

MATLAB Name mip.tolerances.relobjdifference

Interactive Optimizer mip tolerances relobjdifference

Identifier 2022

Description

Used to update the cutoff each time a mixed integer solution is found. The value ismultiplied by the absolute value of the integer objective and subtracted from(added to) the newly found integer objective when minimizing (maximizing). Thiscomputation forces the mixed integer optimization to ignore integer solutions thatare not at least this amount better than the one found so far.

The relative objective difference parameter can be adjusted to improve problemsolving efficiency by limiting the number of nodes; however, setting this parameterat a value other than zero (the default) can cause some integer solutions, includingthe true integer optimum, to be missed.

If both the relative objective difference and the “absolute objective differencecutoff” on page 87 (CPX_PARAM_OBJDIF, ObjDif) are nonzero, the value of theabsolute objective difference is used.

Values

Any number from 0.0 to 1.0; default: 0.0.


See also


number of attempts to repair infeasible MIP startLimits the attempts to repair an infeasible MIP start.

Purpose

Number of attempts to repair infeasible MIP startC Name CPX_PARAM_REPAIRTRIES (long)

C++ Name RepairTries (long)

Java Name RepairTries (long)

.NET Name RepairTries (long)

OPL Name repairtries

Python Name mip.limits.repairtries

MATLAB Name mip.limits.repairtries

Interactive Optimizer mip limits repairtries

Identifier 2067

Description

Limits the attempts to repair an infeasible MIP start. This parameter lets you tellCPLEX whether and how many times it should try to repair an infeasible MIP startthat you supplied. The parameter has no effect if the MIP start you supplied isfeasible. It has no effect if no MIP start was supplied.

Table 58. Values

Value Meaning

-1 None: do not try to repair


Any positive integer Number of attempts

MIP repeat presolve switchSpecifies whether to re-apply presolve, with or without cuts, to a MIP model afterprocessing at the root is otherwise complete.

Purpose

Reapply presolve after processing the root nodeC Name CPX_PARAM_REPEATPRESOLVE (int)

C++ Name RepeatPresolve (int)

Java Name RepeatPresolve (int)

.NET Name RepeatPresolve (int)

OPL Name repeatpresolve

Python Name preprocessing.repeatpresolve

MATLAB Name preprocessing.repeatpresolve

Interactive Optimizer preprocessing repeatpresolve

Identifier 2064


Description

Specifies whether to re-apply presolve, with or without cuts, to a MIP model afterprocessing at the root is otherwise complete.

Table 59. Values

Value Symbol


0 Turn off represolve

1 Represolve without cuts

2 Represolve with cuts

3 Represolve with cuts and allow new rootcuts

RINS heuristic frequencyDecides how often to apply the relaxation induced neighborhood search (RINS)heuristic.

Purpose

RINS heuristic frequencyC Name CPX_PARAM_RINSHEUR (long)

C++ Name RINSHeur (long)

Java Name RINSHeur (long)

.NET Name RINSHeur (long)

OPL Name rinsheur

Python Name mip.strategy.rinsheur

MATLAB Name mip.strategy.rinsheur

Interactive Optimizer mip strategy rinsheur

Identifier 2061

Description

Decides how often to apply the relaxation induced neighborhood search (RINS)heuristic. This heuristic attempts to improve upon the best solution found so far. Itwill not be applied until CPLEX has found at least one incumbent solution.

Setting the value to -1 turns off the RINS heuristic. Setting the value to 0 (zero), thedefault, applies the RINS heuristic at an interval chosen automatically by CPLEX.Setting the value to a positive number applies the RINS heuristic at the requestednode interval. For example, setting RINSHeur to 20 dictates that the RINS heuristicbe called at node 0, 20, 40, 60, etc.

RINS is a powerful heuristic for finding high quality feasible solutions, but it maybe expensive.

Table 60. Values

Value Meaning

-1 None: do not apply RINS heuristic




Value Meaning

Any positive integer Frequency to apply RINS heuristic

algorithm for continuous problemsControls which algorithm is used to solve continuous models or to solve the rootrelaxation of a MIP.

Purpose

Solution algorithm for continuous problemsC Name CPX_PARAM_LPMETHOD (int)

C++ Name RootAlg (int)

Java Name RootAlg (int)

.NET Name RootAlg (int)

OPL Name rootalg

Python Name lpmethod

MATLAB Name lpmethod in Cplex Class API

MATLAB Name lpmethod in Toolbox (CPLEX compatible)

MATLAB Name Simplex in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer lpmethod

Identifier 1062

Description

Controls which algorithm CPLEX uses to solve continuous models (LPs).

In the object-oriented APIs (such as C++, Java, or .NET APIs), this parameter, asRootAlg, also controls which algorithm CPLEX uses to solve the root relaxation of aMIP.

In the C API and the Interactive Optimizer, there are separate parameters tocontrol LP, QP, and MIP optimizers, depending on the problem type. See, forexample, “algorithm for continuous quadratic optimization” on page 114 or “MIPstarting algorithm” on page 115.

In all cases, the default setting is 0 (zero). The default setting means that CPLEXwill select the algorithm in a way that should give best overall performance.

For specific problem classes, the following details document the automatic settings.Note that future versions of CPLEX could adopt different strategies. Therefore, ifyou select any nondefault settings, you should review them periodically.

Currently, the behavior of the automatic setting is that CPLEX almost alwaysinvokes the dual simplex algorithm when it is solving an LP model from scratch.When it is continuing from an advanced basis, it will check whether the basis isprimal or dual feasible, and choose the primal or dual simplex algorithmaccordingly.


If multiple threads have been requested, in either deterministic or opportunisticmode, the concurrent optimization algorithm is selected by the automatic settingwhen CPLEX is solving a continuous linear programming model (LP) from scratch.

When three or more threads are available, and you select concurrent optimizationfor the value of this parameter, its behavior depends on whether parallel mode isopportunistic or deterministic (default parallel mode). Concurrent optimization inopportunistic parallel mode runs the dual simplex optimizer on one thread, theprimal simplex optimizer on a second thread, the parallel barrier optimizer on athird thread and on any additional available threads. In contrast, concurrentoptimization in deterministic parallel mode runs the dual optimizer on one threadand the parallel barrier optimizer on any additional available threads.

The automatic setting may be expanded in the future so that CPLEX chooses thealgorithm based on additional problem characteristics.

Table 61. Values


0 CPX_ALG_AUTOMATIC Automatic: let CPLEXchoose; default

1 CPX_ALG_PRIMAL Primal simplex

2 CPX_ALG_DUAL Dual simplex

3 CPX_ALG_NET Network simplex



6 CPX_ALG_CONCURRENT Concurrent (Dual, Barrier,and Primal in opportunisticparallel mode; Dual andBarrier in deterministicparallel mode)

algorithm for continuous quadratic optimizationSets which algorithm to use when the C routine CPXqpopt (or the commandoptimize in the Interactive Optimizer) is invoked.

Purpose

Algorithm for continuous quadratic optimizationC Name CPX_PARAM_QPMETHOD (int)




OPL Name rootalg

Python Name qpmethod

MATLAB Name qpmethod

Interactive Optimizer qpmethod

Identifier 1063


Description

Sets which algorithm to use when the C routine CPXqpopt (or the commandoptimize in the Interactive Optimizer) is invoked.

Currently, the behavior of the Automatic setting is that CPLEX invokes the BarrierOptimizer for continuous QP models. The Automatic setting may be expanded inthe future so that CPLEX chooses the algorithm based on additional problemcharacteristics.

Table 62. Values


0 CPX_ALG_AUTOMATIC Automatic: let CPLEXchoose; default

1 CPX_ALG_PRIMAL Use the primal simplexoptimizer.

2 CPX_ALG_DUAL Use the dual simplexoptimizer.

3 CPX_ALG_NET Use the network optimizer.

4 CPX_ALG_BARRIER Use the barrier optimizer.

MIP starting algorithmSets which continuous optimizer will be used to solve the initial relaxation of aMIP.

Purpose

MIP starting algorithmC Name CPX_PARAM_STARTALG (int)




OPL Name rootalg

Python Name mip.strategy.startalgorithm

MATLAB Name mip.strategy.startalgorithm

Interactive Optimizer mip strategy startalgorithm

Identifier 2025

Description

Sets which continuous optimizer will be used to solve the initial relaxation of aMIP.

The default Automatic setting (0 zero) of this parameter currently selects theconcurrent optimizer for root relaxations of mixed integer linear programmingmodels (MILP) and selects the dual simplex optimizer for root relaxations of mixedinteger quadratic programming models (MIQP). The Automatic setting may beexpanded in the future so that CPLEX chooses the algorithm based on additionalcharacteristics of the model.


For MILP (integer constraints and otherwise continuous variables), all settings arepermitted.

For MIQP (integer constraints and positive semi-definite quadratic terms in theobjective), settings 5 (Sifting) and 6 (Concurrent) are not implemented; if youhappen to choose them, setting 5 (Sifting) reverts to 0 (zero) and setting 6(Concurrent) reverts to 4.

For MIQCP (integer constraints and positive semi-definite quadratic terms amongthe constraints), only the barrier optimizer is implemented, and therefore nosettings other than 0 (zero) and 4 are permitted.

Table 63. Values


0 CPX_ALG_AUTOMATIC Automatic: let CPLEX choose;default

1 CPX_ALG_PRIMAL Primal Simplex

2 CPX_ALG_DUAL Dual Simplex

3 CPX_ALG_NET Network Simplex



6 CPX_ALG_CONCURRENT Concurrent (Dual, Barrier, andPrimal in opportunistic mode;Dual and Barrier in deterministicmode)

auxiliary root threadsPartitions the number of threads to manage tasks at the root node.

Purpose

Auxiliary root threadsC Name CPX_PARAM_AUXROOTTHREADS (int)

C++ Name AuxRootThreads (int)

Java Name AuxRootThreads (int)

.NET Name AuxRootThreads (int)

OPL Name auxrootthreads

Python Name mip.limits.auxrootthreads

MATLAB Name mip.limits.auxrootthreads

Interactive Optimizer mip limits auxrootthreads n

Identifier 2139

Description

Partitions the number of threads for CPLEX to use for auxiliary tasks while itsolves the root node of a problem.

On a system that offers N global threads, if you set this parameter to n, whereN > n > 0


then CPLEX uses at most n threads for auxiliary tasks and at most N-n threads tosolve the root node.

See also the parameter CPX_PARAM_THREADS, Threads, documented in the topic“global default thread count” on page 131, for more general information aboutparallel solving and threads.

Tip:

You cannot set n, the value of this parameter, to a value greater than or equal to N,the number of global threads offered on your system.

Independent of the auxiliary root threads parameter, CPLEX will never use morethreads than those defined by the “global default thread count” on page 131parameter, whether that parameter is 0 (zero), its default value, or N, a value thatyou set. CPLEX also makes sure that there is at least one thread available for themain root tasks. For example, if you set the global threads parameter to 3 and theauxiliary root threads parameter to 4, CPLEX still uses only two threads forauxiliary root tasks in order to keep one thread available for the main root tasks.

At its default value, 0 (zero), CPLEX automatically chooses the number of threadsto use for the primary root tasks and for auxiliary tasks. The number of threadsthat CPLEX uses to solve the root node depends on these factors:v the number of threads available to your application on your system (for

example, as a result of limited resources or competition with other applications);v the value of the “global default thread count” on page 131 parameter

(CPX_PARAM_THREADS, Threads).

Table 64. Values

Value Meaning

-1 Off: do not use additional threads forauxiliary tasks.

0 Automatic: let CPLEX choose the number ofthreads to use; default

N > n > 0 Use n threads for auxiliary root tasks

constraint (row) read limitSpecifies a limit for the number of rows (constraints) to read for an allocation ofmemory.

Purpose

Constraint (row) read limitC Name CPX_PARAM_ROWREADLIM (int)

C++ Name RowReadLim (int)

Java Name RowReadLim (int)

.NET Name RowReadLim (int)

Python Name read.constraints

MATLAB Name read.constraints

Interactive Optimizer read constraints

Identifier 1021


Description

Specifies a limit for the number of rows (constraints) to read for an allocation ofmemory.


Values

Any integer from 0 (zero) to CPX_BIGINT ; default: 30 000.

scale parameterDecides how to scale the problem matrix.

Purpose

Scale parameterC Name CPX_PARAM_SCAIND (int)

C++ Name ScaInd (int)

Java Name ScaInd (int)

.NET Name ScaInd (int)

OPL Name scaind

Python Name read.scale

MATLAB Name read.scale

Interactive Optimizer read scale

Identifier 1034

Description

Decides how to scale the problem matrix.

Table 65. Values

Value Meaning

-1 No scaling

0 Equilibration scaling; default

1 More aggressive scaling

messages to screen switchDecides whether or not results are displayed on screen in an application of the CAPI.

Purpose

Messages to screen switchC Name CPX_PARAM_SCRIND (int)

C++ Name screen indicator not available in this API

Java Name screen indicator not available in this API

.NET Name screen indicator not available in this API


Python Name screen indicator not available in this API

MATLAB Name DisplayFunc in Cplex Class API

MATLAB Name Diagnostics or diagnostics in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer screen indicator not available in this interface

Identifier 1035

Description

Decides whether or not results are displayed on screen in an application of theCallable Library (C API). This parameter works by adding or removing stdout toor from the result, warning, and error channels. Consequently, good practice doesnot manage stdout in those channels directly at the same time as using thisparameter; otherwise, undefined behavior can occur.

To turn off output to the screen, in a C++ application, where cplex is an instanceof the class IloCplex and env is an instance of the class IloEnv , the environment,use cplex.setOut(env.getNullStream()) .

In a Java application, use cplex.setOut(null).

In a .NET application, use Cplex.SetOut(Null).

In a Python application, where c is an instance of the class cplex.Cplex, usec.set_results_stream(None).

Table 66. Values


0 CPX_OFF Turn off display of messagesto screen; default

1 CPX_ON Display messages on screen

sifting subproblem algorithmSets the algorithm to be used for solving sifting subproblems.

Purpose

Sifting subproblem algorithmC Name CPX_PARAM_SIFTALG (int)

C++ Name SiftAlg (int)

Java Name SiftAlg (int)

.NET Name SiftAlg (int)

OPL Name siftalg

Python Name sifting.algorithm

MATLAB Name sifting.algorithm

Interactive Optimizer sifting algorithm

Identifier 1077

Description

Sets the algorithm to be used for solving sifting subproblems. The defaultautomatic setting will typically use a mix of barrier and primal simplex.


Table 67. Values


0 CPX_ALG_AUTOMATIC Automatic: let CPLEX choose; default

1 CPX_ALG_PRIMAL Primal Simplex

2 CPX_ALG_DUAL Dual Simplex

3 CPX_ALG_NET Network Simplex


sifting information displaySets the amount of information to display about the progress of sifting.

Purpose

Sifting information displayC Name CPX_PARAM_SIFTDISPLAY (int)

C++ Name SiftDisplay (int)

Java Name SiftDisplay (int)

.NET Name SiftDisplay (int)

OPL Name siftdisplay

Python Name sifting.display

MATLAB Name sifting.display

Interactive Optimizer sifting display

Identifier 1076

Description

Sets the amount of information to display about the progress of sifting.

Table 68. Values

Value Meaning

0 No display of sifting information

1 Display major iterations; default

2 Display LP subproblem information withineach sifting iteration

upper limit on sifting iterationsSets the maximum number of sifting iterations that may be performed ifconvergence to optimality has not been reached.

Purpose

Upper limit on sifting iterationsC Name CPX_PARAM_SIFTITLIM (long)

C++ Name SiftItLim (long)

Java Name SiftItLim (long)

.NET Name SiftItLim (long)

OPL Name siftitlim


Python Name sifting.iterations

MATLAB Name sifting.iterations

Interactive Optimizer sifting iterations

Identifier 1078

Description

Sets the maximum number of sifting iterations that may be performed ifconvergence to optimality has not been reached.

Values


simplex iteration information displaySets how often CPLEX reports about iterations during simplex optimization.

Purpose

Simplex iteration information displayC Name CPX_PARAM_SIMDISPLAY (int)

C++ Name SimDisplay (int)

Java Name SimDisplay (int)

.NET Name SimDisplay (int)

OPL Name simdisplay

Python Name simplex.display

MATLAB Name simplex.display

Interactive Optimizer simplex display

Identifier 1019

Description

Sets how often CPLEX reports about iterations during simplex optimization.

Table 69. Values

Value Meaning

0 No iteration messages until solution

1 Iteration information after each refactoring;default

2 Iteration information for each iteration

simplex singularity repair limitRestricts the number of times CPLEX attempts to repair the basis whensingularities are encountered during the simplex algorithm.

Purpose

Simplex singularity repair limitC Name CPX_PARAM_SINGLIM (int)

C++ Name SingLim (int)


Java Name SingLim (int)

.NET Name SingLim (int)

OPL Name singlim

Python Name simplex.limits.singularity

MATLAB Name simplex.limits.singularity

Interactive Optimizer simplex limits singularity

Identifier 1037

Description

Restricts the number of times CPLEX attempts to repair the basis whensingularities are encountered during the simplex algorithm. When this limit isexceeded, CPLEX replaces the current basis with the best factorable basis that hasbeen found.

Values


absolute gap for solution poolSets an absolute tolerance on the objective value for the solutions in the solutionpool.

Purpose

Absolute gap for solution poolC Name CPX_PARAM_SOLNPOOLAGAP (double)

C++ Name SolnPoolAGap (double)

Java Name SolnPoolAGap (double)

.NET Name SolnPoolAGap (double)

OPL Name solnpoolagap

Python Name mip.pool.absgap

MATLAB Name mip.pool.absgap

Interactive Optimizer mip pool absgap

Identifier 2106

Description

Sets an absolute tolerance on the objective value for the solutions in the solutionpool. Solutions that are worse (either greater in the case of a minimization, or lessin the case of a maximization) than the objective of the incumbent solutionaccording to this measure are not kept in the solution pool.

Values of the solution pool absolute gap (SolnPoolAGap or CPX_PARAM_SOLNPOOLAGAP)and the solution pool relative gap (“relative gap for solution pool” on page 124:SolnPoolGap or CPX_PARAM_SOLNPOOLGAP) may differ: For example, you may specifythat solutions must be within 15 units by means of the solution pool absolute gapand also within 1% of the incumbent by means of the solution pool relative gap. Asolution is accepted in the pool only if it is valid for both the relative and theabsolute gaps.


The solution pool absolute gap parameter can also be used as a stopping criterionfor the populate procedure: if populate cannot enumerate any more solutions thatsatisfy this objective quality, then it will stop. In the presence of both an absoluteand a relative solution pool gap parameter, populate will stop when the smaller ofthe two is reached.

Values

Any nonnegative real number; default: 1.0e+75.

maximum number of solutions kept in solution poolLimits the number of solutions kept in the solution pool

Purpose

Maximum number of solutions kept in the solution poolC Name CPX_PARAM_SOLNPOOLCAPACITY (int)

C++ Name SolnPoolCapacity (int)

Java Name SolnPoolCapacity (int)

.NET Name SolnPoolCapacity (int)

OPL Name solnpoolcapacity

Python Name mip.pool.capacity

MATLAB Name mip.pool.capacity

Interactive Optimizer mip pool capacity

Identifier 2103

Description

Sets the maximum number of solutions kept in the solution pool. At most,SolnPoolCapacity solutions will be stored in the pool. Superfluous solutions aremanaged according to the strategy set by the “solution pool replacement strategy”on page 126 parameter (SolnPoolReplace, CPX_PARAM_SOLNPOOLREPLACE).

The optimization (whether by MIP optimization or the populate procedure) willnot stop if more than SolnPoolCapacity solutions are generated. Instead, stoppingcriteria can be specified by these parameters:v “maximum number of solutions generated for solution pool by populate” on

page 98 (PopulateLim, CPX_PARAM_POPULATELIM)v “relative gap for solution pool” on page 124 (SolnPoolGap,

CPX_PARAM_SOLNPOOLGAP)v “absolute gap for solution pool” on page 122 (SolnPoolAGap,

CPX_PARAM_SOLNPOOLAGAP)v “MIP node limit” on page 85 (NodeLim, CPX_PARAM_NODELIM)v “optimizer time limit in seconds” on page 132 (TiLim, CPX_PARAM_TILIM)

The default value for SolnPoolCapacity is 2100000000, but it may be set to anynonnegative integer value. If set to zero, it will turn off all features related to thesolution pool.

If you are looking for a parameter to control the number of solutions generated bythe populate procedure, consider the parameter “maximum number of solutionsgenerated for solution pool by populate” on page 98.


Values

Any nonnegative integer; 0 (zero) turns off all features of the solution pool;default: 2100000000.

relative gap for solution poolSets a relative tolerance on the objective value for the solutions in the solutionpool.

Purpose

Relative gap for the solution poolC Name CPX_PARAM_SOLNPOOLGAP (double)

C++ Name SolnPoolGap (double)

Java Name SolnPoolGap (double)

.NET Name SolnPoolGap (double)

OPL Name solnpoolgap

Python Name mip.pool.relgap

MATLAB Name mip.pool.relgap

Interactive Optimizer mip pool relgap

Identifier 2105

Description

Sets a relative tolerance on the objective value for the solutions in the solutionpool. Solutions that are worse (either greater in the case of a minimization, or lessin the case of a maximization) than the incumbent solution by this measure are notkept in the solution pool. For example, if you set this parameter to 0.01, thensolutions worse than the incumbent by 1% or more will be discarded.

Values of the “absolute gap for solution pool” on page 122 (SolnPoolAGap orCPX_PARAM_SOLNPOOLAGAP) and the “relative gap for solution pool” (SolnPoolGap orCPX_PARAM_SOLNPOOLGAP) may differ: For example, you may specify that solutionsmust be within 15 units by means of the solution pool absolute gap and within 1%of the incumbent by means of the solution pool relative gap. A solution is acceptedin the pool only if it is valid for both the relative and the absolute gaps.

The solution pool relative gap parameter can also be used as a stopping criterionfor the populate procedure: if populate cannot enumerate any more solutions thatsatisfy this objective quality, then it will stop. In the presence of both an absoluteand a relative solution pool gap parameter, populate will stop when the smaller ofthe two is reached.

Values

Any nonnegative real number; default: 1.0e+75.

solution pool intensityControls the trade-off between the number of solutions generated for the solutionpool and the amount of time or memory consumed.


Purpose

Solution pool intensityC Name CPX_PARAM_SOLNPOOLINTENSITY (int)

C++ Name SolnPoolIntensity (int)

Java Name SolnPoolIntensity (int)

.NET Name SolnPoolIntensity (int)

OPL Name solnpoolintensity

Python Name mip.pool.intensity

MATLAB Name mip.pool.intensity

Interactive Optimizer mip pool intensity

Identifier 2107

Description

Controls the trade-off between the number of solutions generated for the solutionpool and the amount of time or memory consumed. This parameter applies both toMIP optimization and to the populate procedure.

Values from 1 (one) to 4 invoke increasing effort to find larger numbers ofsolutions. Higher values are more expensive in terms of time and memory but arelikely to yield more solutions.

Effect

For MIP optimization, increasing the value of the parameter corresponds toincreasing the amount of effort spent setting up the branch and cut tree to preparefor a subsequent call to the populate procedure.

For populate, increasing the value of this parameter corresponds, in addition, toincreasing the amount of effort spent exploring the tree to generate more solutions.If MIP optimization is called before populate, populate will reuse the informationcomputed and stored during MIP optimization only if this parameter has not beenincreased between calls. Similarly, if populate is called several times successively,populate will re-use the information computed and stored during previous calls topopulate only if the solution pool intensity has not increased between calls.Therefore, it is most efficient not to change the value of this parameter betweencalls to MIP optimization and populate, nor between successive calls of populate.Increase the value of this parameter only if too few solutions are generated.

Settings

Its default value, 0 (zero), lets CPLEX choose which intensity to apply. If MIPoptimization is called first after the model is read, CPLEX sets the intensity to1 (one) for this call to MIP optimization and to subsequent calls of populate. Incontrast, if populate is called directly after the model is read, CPLEX sets theintensity to 2 for this call and subsequent calls of populate.

For value 1 (one), the performance of MIP optimization is not affected. There is noslowdown and no additional consumption of memory due to this setting.However, populate will quickly generate only a small number of solutions.Generating more than a few solutions with this setting will be slow. When you arelooking for a larger number of solutions, use a higher value of this parameter.


For value 2, some information is stored in the branch and cut tree so that it iseasier to generate a larger number of solutions. This storage has an impact onmemory used but does not lead to a slowdown in the performance of MIPoptimization. With this value, calling populate is likely to yield a number ofsolutions large enough for most purposes. This value is a good choice for mostmodels.

For value 3, the algorithm is more aggressive in computing and storinginformation in order to generate a large number of solutions. Compared to values1 (one) and 2, this value will generate a larger number of solutions, but it will slowMIP optimization and increase memory consumption. Use this value only if settingthis parameter to 2 does not generate enough solutions.

For value 4, the algorithm generates all solutions to your model. Even for smallmodels, the number of possible solutions is likely to be huge; thus enumerating allof them will take time and consume a large quantity of memory. In this case,remember to set the “maximum number of solutions generated for solution poolby populate” on page 98 (PopulateLim, CPX_PARAM_POPULATELIM) to a valueappropriate for your model; otherwise, the populate procedure will stopprematurely because of this stopping criterion instead of enumerating all solutions.In addition, a few limitations apply to this exhaustive enumeration, as explained inEnumerating all solutions in the CPLEX User’s Manual.

Table 70. Values

Value Meaning


1 Mild: generate few solutions quickly

2 Moderate: generate a larger number ofsolutions

3 Aggressive: generate many solutions andexpect performance penalty

4 Very aggressive: enumerate all practicalsolutions

solution pool replacement strategyDesignates the strategy for replacing a solution in the solution pool when thesolution pool has reached its capacity.

Purpose

Solution pool replacement strategyC Name CPX_PARAM_SOLNPOOLREPLACE (int)

C++ Name SolnPoolReplace (int)

Java Name SolnPoolReplace (int)

.NET Name SolnPoolReplace (int)

OPL Name solnpoolreplace

Python Name mip.pool.replace

MATLAB Name mip.pool.replace

Interactive Optimizer mip pool replace

Identifier 2104


Description

Designates the strategy for replacing a solution in the solution pool when thesolution pool has reached its capacity.

The value 0 (CPX_SOLNPOOL_FIFO ) replaces solutions according to a first-in, first-outpolicy. The value 1 (CPX_SOLNPOOL_OBJ ) keeps the solutions with the best objectivevalues. The value 2 (CPX_SOLNPOOL_DIV ) replaces solutions in order to build a set ofdiverse solutions.

If the solutions you obtain are too similar to each other, try settingSolnPoolReplace to 2.

The replacement strategy applies only to the subset of solutions created in thecurrent call of MIP optimization or populate. Solutions already in the pool are notaffected by the replacement strategy. They will not be replaced, even if they satisfythe criterion of the replacement strategy.

Table 71. Values


0 CPX_SOLNPOOL_FIFO Replace the first solution(oldest) by the most recentsolution; first in, first out;default

1 CPX_SOLNPOOL_OBJ Replace the solution whichhas the worst objective

2 CPX_SOLNPOOL_DIV Replace solutions in order tobuild a set of diversesolutions

solution target typeSpecifies type of solution CPLEX targets (optimal convex or first-order satisfaction)

Purpose

Solution target typeC Name CPX_PARAM_SOLUTIONTARGET (int)

C++ Name SolutionTarget (int)

Java Name SolutionTarget (int)

.NET Name SolutionTarget (int)

OPL Name solutiontarget

Python Name solutiontarget

MATLAB Name solutiontarget

Interactive Optimizer solutiontarget

Identifier 1131

Description

Specifies the type of solution CPLEX attempts to compute when CPLEX solves anonconvex, continuous quadratic model. This parameter affects the behavior ofCPLEX only when CPLEX uses the barrier algorithm without crossover to solve anonconvex continuous quadratic model (QP); that is, the variables of the model are


continuous, the objective function includes a quadratic term, and the objectivefunction is not positive semi-definite (PSD).

By default, CPLEX first attempts to compute a provably optimal solution. IfCPLEX cannot compute a provably optimal solution because the objective functionis not convex, CPLEX will return the error CPXERR_Q_NOT_POS_DEF.

When this parameter is set to 1 (one), CPLEX searches for a globally optimalsolution to a convex model.

When this parameter is set to 2, CPLEX first attempts to compute a provablyoptimal solution. If CPLEX cannot compute a provably optimal solution becausethe objective function is not convex, CPLEX searches for a solution that satisfiesfirst-order optimality conditions but is not necessarily globally optimal. In such acase, you can query the solution status to determine the kind of solution CPLEXfound.

Table 72. Values


0 CPX_SOLUTIONTARGET_AUTO Automatic: let CPLEX decide;default

1 CPX_SOLUTIONTARGET_OPTIMALCONVEX Search for a globally optimalsolution to a convex model.

2 CPX_SOLUTIONTARGET_FIRSTORDER Search for a solution that satisfiesfirst-order optimality conditions,but is not necessarily globallyoptimal.

MIP strong branching candidate list limitControls the length of the candidate list when CPLEX uses variable selection as thesetting for strong branching.

Purpose

MIP strong branching candidate list limitC Name CPX_PARAM_STRONGCANDLIM (int)

C++ Name StrongCandLim (int)

Java Name StrongCandLim (int)

.NET Name StrongCandLim (int)

OPL Name strongcandlim

Python Name mip.limits.strongcand

MATLAB Name mip.limits.strongcand

Interactive Optimizer mip limits strongcand

Identifier 2045

Description

Controls the length of the candidate list when CPLEX uses strong branching as theway to select variables. For more detail about that parameter, see “MIP variableselection strategy” on page 139:v VarSel in the C++, Java, or .NET API;


v CPX_PARAM_VARSEL in the C API;v set mip strategy variableselect 3 in the Interactive Optimizer.

Values

Any positive number; default: 10.

MIP strong branching iterations limitControls the number of simplex iterations performed on each variable in thecandidate list when CPLEX uses variable selection as the setting for strongbranching.

Purpose

MIP strong branching iterations limitC Name CPX_PARAM_STRONGITLIM (long)

C++ Name StrongItLim (long)

Java Name StrongItLim (long)

.NET Name StrongItLim (long)

OPL Name strongitlim

Python Name mip.limits.strongit

MATLAB Name mip.limits.strongit

Interactive Optimizer mip limits strongit

Identifier 2046

Description

Controls the number of simplex iterations performed on each variable in thecandidate list when CPLEX uses strong branching as the way to select variables.For more detail about that parameter, see “MIP variable selection strategy” on page139:v VarSel in the C++, Java, or .NET API;v CPX_PARAM_VARSEL in the C API;v set mip strategy variableselect 3 in the Interactive Optimizer.

The default setting 0 (zero) chooses the iteration limit automatically.

Table 73. Values

Value Meaning


Any positive integer Limit of the simplex iterations performed oneach candidate variable

limit on nodes explored when a subMIP is being solvedRestricts the number of nodes explored when CPLEX is solving a subMIP.

Purpose

Limit on nodes explored when a subMIP is being solvedC Name CPX_PARAM_SUBMIPNODELIM (long)


C++ Name SubMIPNodeLim (long)

Java Name SubMIPNodeLim (long)

.NET Name SubMIPNodeLim (long)

OPL Name submipnodelim

Python Name mip.limits.submipnodelim

MATLAB Name mip.limits.submipnodelim

Interactive Optimizer mip limits submipnodelim

Identifier 2062

Description

Restricts the number of nodes explored when CPLEX is solving a subMIP.

CPLEX solves subMIPs in these situations:v when it builds a solution from a partial MIP start;v when it repairs an infeasible MIP start;v when it executes the relaxation induced neighborhood search (RINS) heuristic;v when it branches locally;v when it polishes a solution.

Values

Any positive integer; default: 500.

symmetry breakingDecides whether symmetry breaking reductions will be automatically executed,during the preprocessing phase, in a MIP model.

Purpose

Symmetry breakingC Name CPX_PARAM_SYMMETRY (int)

C++ Name Symmetry (int)

Java Name Symmetry (int)

.NET Name Symmetry (int)

OPL Name symmetry

Python Name preprocessing.symmetry

MATLAB Name preprocessing.symmetry

Interactive Optimizer preprocessing symmetry

Identifier 2059

Description

Decides whether symmetry breaking reductions will be automatically executed,during the preprocessing phase, in a MIP model. The default level, -1, allowsCPLEX to choose the degree of symmetry breaking to apply. The value 0 (zero)turns off symmetry breaking. Levels 1 through 5 apply increasingly aggressivesymmetry breaking.


Table 74. Values

Value Meaning


0 Turn off symmetry breaking

1 Exert a moderate level of symmetrybreaking

2 Exert an aggressive level of symmetrybreaking

3 Exert a very aggressive level of symmetrybreaking

4 Exert a highly aggressive level of symmetrybreaking

5 Exert an extremely aggressive level ofsymmetry breaking

global default thread countSets the default number of parallel threads that will be invoked by any CPLEXparallel optimizer.

Purpose

Global default thread countC Name CPX_PARAM_THREADS (int)

C++ Name Threads (int)

Java Name Threads (int)

.NET Name Threads (int)

OPL Name threads

Python Name threads

MATLAB Name threads

Interactive Optimizer threads

Identifier 1067

Description

Sets the default maximal number of parallel threads that will be invoked by anyCPLEX parallel optimizer.

For a single thread, the parallel algorithms behave deterministically, regardless ofthread parameter settings; that is, the algorithm proceeds sequentially in a singlethread.

In this context, sequential means that the algorithm proceeds step by step,consecutively, in a predictable and repeatable order within a single thread.Deterministic means that repeated solving of the same model with the sameparameter settings on the same computing platform will follow exactly the samesolution path, yielding the same level of performance and the same values in thesolution. Sequential execution is deterministic. In multithreaded computing, adeterministic setting requires synchronization between threads. Opportunisticentails less synchronization between threads and thus may offer better


performance at the sacrifice of repeatable, invariant solution paths and values inrepeated runs on multiple threads or multiple processors.

When this parameter is at its default setting 0 (zero), and your application includesno callbacks or only an informational callback, CPLEX can use all availablethreads; that is, at most 32 threads or the number of cores of the machine,whichever is smaller. If your machine offers more than 32 threads, you can takeadvantage of them by increasing the value of this parameter.

When this parameter is at its default setting 0 (zero), and your application includescallbacks other than informational callbacks (that is, the application includes aquery, diagnostic, or control callback), then CPLEX uses one thread. In otherwords, the presence of a callback turns off parallel processing when the value ofthis parameter is at its default.

In order to use parallel optimization in conjunction with callbacks, you need toset this parameter to a positive value. However, when you do so, you need to beaware of the fact that the callbacks may be invoked concurrently.

For a description of informational, query, diagnostic, and control callbacks, see thetopic Using optimization callbacks in the CPLEX User’s Manual.

Table 75. Values

Value Meaning

0 Automatic: let CPLEX decide; default

1 Sequential; single threaded

N Uses up to N threads; N is limited by available processors and ProcessorValue Units (PVU).

See also

“parallel mode switch” on page 90

optimizer time limit in secondsSets the maximum time, in seconds, for a call to an optimizer.

Purpose

Optimizer time limit in secondsC Name CPX_PARAM_TILIM (double)

C++ Name TiLim (double)

Java Name TiLim (double)

.NET Name TiLim (double)

OPL Name tilim

Python Name timelimit

MATLAB Name timelimit in Cplex Class API

MATLAB Name timelimit in Toolbox (CPLEX compatible)

MATLAB Name MaxTime in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer timelimit

Identifier 1039


Description

Sets the maximum time, in seconds, for a call to an optimizer. This time limitapplies also to the conflict refiner.

The time is measured in terms of either CPU time or elapsed time, according to thesetting of the “clock type for computation time” on page 35 parameter(CPX_PARAM_CLOCKTYPE, ClockType).

The time limit for an optimizer applies to the sum of all its steps, such aspreprocessing, crossover, and internal calls to other optimizers.

In a sequence of calls to optimizers, the limit is not cumulative but applies to eachcall individually. For example, if you set a time limit of 10 seconds, and you callMIP optimization twice then there could be a total of (at most) 20 seconds ofrunning time if each call consumes its maximum allotment.

See also

For a deterministic time limit on optimization, see “deterministic time limit” onpage 44 (CPX_PARAM_DETTILIM, DetTiLim).

For an introduction to time stamps measured in seconds, see the topic Timinginterface in the CPLEX User's Manual. For more detail about time stamps measuredin seconds, see the reference manual of the API that you use.v In the Callable Library (C API), see the documentation of CPXXgettime and

CPXXgetcallbackinfo.v In the C++ API, see the documentation of IloCplex::CallbackI::getStartTime and

IloCplex::CallbackI::getEndTime.v In the Java API, see the documentation of IloCplex.Callback.getStartTime and

IloCplex.Callback.getEndTime.v In the .NET API, see the documentation of Cplex.ICallback.GetStartTime and

GetEndTime.v In the Python API, see the documentation of Callback.get_start_time and

Callback.get_end_time.v In the MATLAB connector, see the documentation of cplex.Solution.time.

Values

Any nonnegative value in seconds; default: 1e+75.

See also


tree memory limitSets an absolute upper limit on the size (in megabytes, uncompressed) of thebranch-and-cut tree.

Purpose

Tree memory limitC Name CPX_PARAM_TRELIM (double)


C++ Name TreLim (double)

Java Name TreLim (double)

.NET Name TreLim (double)

OPL Name trelim

Python Name mip.limits.treememory

MATLAB Name mip.limits.treememory

Interactive Optimizer mip limits treememory

Identifier 2027

Description

Sets an absolute upper limit on the size (in megabytes, uncompressed) of thebranch-and-cut tree. If this limit is exceeded, CPLEX terminates optimization.

Values


deterministic tuning time limitSets a time limit in deterministic ticks per model and per test set (that is, suite ofmodels) applicable in tuning.

Purpose

Deterministic tuning time limitC Name CPX_PARAM_TUNINGDETTILIM (double)

C++ Name TuningDetTiLim (double)

Java Name TuningDetTiLim (double)

.NET Name TuningDetTiLim (double)

OPL Name tuningdettilim

Python Name tuning.dettimelimit

MATLAB Name tuning.timelimit

Interactive Optimizer tune dettimelimit

Identifier 1139

Description

Sets a deterministic time limit per model and per test set (that is, suite of models)applicable in tuning and measured in ticks.

When this deterministic tuning time limit is set to a finite value, then tuning findsappropriate settings of other CPLEX parameters to minimize the deterministic timeof optimization. Furthermore, the tuning process itself is deterministic. In thiscontext, "a finite value" means any value strictly less than 1e+75 (such as the finitevalue 1e+74).

Interaction with other parameters: nondeterministic tuning timelimit

This deterministic time limit on tuning is not compatible with the wall-clock“tuning time limit in seconds” on page 138 (CPX_PARAM_TUNINGTILIM,TuningTiLim). Only one of these two parameters can be set to a finite value at a


time. Any attempt to set either of these parameters to a finite value while the otheris already set to a finite value results in the errorCPXERR_PARAM_INCOMPATIBLE from the routine CPXsetdblparam or themethod setDblParam (depending on the API you are using).

Finite values of tuning time limits

If this deterministic time limit on tuning is set to a finite value, then the tuningprocess itself is deterministic, and CPLEX recommends appropriate parametersettings to minimize the deterministic optimization time.

If the wall-clock “tuning time limit in seconds” on page 138(CPX_PARAM_TUNINGTILIM, TuningTiLim) is set to a finite value, then thetuning process itself is nondeterministic, and it recommends appropriateparameter settings to minimize the wall-clock optimization time.

The default value of this parameter is 1e+75 (effectively, infinite).

Likewise, the default value of the wall-clock “tuning time limit in seconds” onpage 138 (CPX_PARAM_TUNINGTILIM, TuningTiLim) is also 1e+75 (effectively,infinite).

If this parameter is set at its default value 1e+75, and if the “tuning time limit inseconds” on page 138 (CPX_PARAM_TUNINGTILIM, TuningTiLim) is also set atits default value 1e+75, then the combination is equivalent to setting thedeterministic tuning time limit to 10 000 000 ticks. Consequently, these combineddefault settings make the tuning process deterministic, and CPLEX recommendssettings to minimize the deterministic optimization time.

Unlimited time per model

If you want to run a tuning session with unlimited time per model, then set one ofthe tuning time limit parameters (either wall-clock “tuning time limit in seconds”on page 138 (CPX_PARAM_TUNINGTILIM, TuningTiLim) or “deterministic tuningtime limit” on page 134 (CPX_PARAM_TUNINGDETTILIM, TuningDetTiLim) to avery large value that is strictly less than 1e+75 (for example, 1e+74). If you setCPX_PARAM_TUNINGDETTILIM, TuningDetTiLim to a finite value, then thetuning process will be deterministic. If you set CPX_PARAM_TUNINGTILIM,TuningTiLim to a finite value, then the tuning process will be nondeterministic.

Ticks

A tick is a unit to measure work done deterministically. The length of adeterministic tick may vary by platform. Nevertheless, ticks are normallyconsistent measures for a given platform (combination of hardware and software)carrying the same load. In other words, the correspondence of ticks to clock timedepends on the hardware, software, and the current load of the machine. For thesame platform and same load, the ratio of ticks per second stays roughly constant,independent of the model solved. However, for very short optimization runs, thevariation of this ratio is typically high.

Values

Any nonnegative number; default: 1e+75 ticks.


See also


“deterministic time limit” on page 44

“tuning time limit in seconds” on page 138

tuning information displaySpecifies the level of information reported by the tuning tool as it works.

Purpose

Tuning information displayC Name CPX_PARAM_TUNINGDISPLAY (int)

C++ Name TuningDisplay (int)

Java Name TuningDisplay (int)

.NET Name TuningDisplay (int)

OPL Name tuningdisplay

Python Name tuning.display

MATLAB Name tune.display

Interactive Optimizer tune display

Identifier 1113

Description

Specifies the level of information reported by the tuning tool as it works.

Use level 0 (zero) to turn off reporting from the tuning tool.

Use level 1 (one), the default, to display a minimal amount of information.

Use level 2 to display the minimal amount plus the parameter settings that thetuning tool is trying.

Use level 3 to display an exhaustive report of minimal information, plus settingsthat are being tried, plus logs.

Table 76. Values

Value Meaning

0 Turn off display

1 Display standard, minimal reporting; default

2 Display standard report plus parametersettings being tried

3 Display exhaustive report and log

tuning measureControls the measure for evaluating progress when a suite of models is beingtuned.


Purpose

Tuning measureC Name CPX_PARAM_TUNINGMEASURE

C++ Name TuningMeasure

Java Name TuningMeasure

.NET Name TuningMeasure

OPL Name tuningmeasure

Python Name tuning.measure

MATLAB Name tuning.measure

Interactive Optimizer tune measure

Identifier 1110

Description

Controls the measure for evaluating progress when a suite of models is beingtuned.

Possible values are:v CPX_TUNE_AVERAGE uses the mean average of time to compare different

parameter sets over a suite of models.v CPX_TUNE_MINMAX uses a minmax approach to compare the time of different

parameter sets over a suite of models.

Table 77. Values

Value Meaning

CPX_TUNE_AVERAGE mean time; default

CPX_TUNE_MINMAX minmax time

tuning repeaterSpecifies the number of times tuning is to be repeated on reordered versions of agiven problem.

Purpose

Tuning repeaterC Name CPX_PARAM_TUNINGREPEAT (int)

C++ Name TuningRepeat (int)

Java Name TuningRepeat (int)

.NET Name TuningRepeat (int)

OPL Name tuningrepeat

Python Name tuning.repeat

MATLAB Name tuning.repeat

Interactive Optimizer tune repeat

Identifier 1111

Description

Specifies the number of times tuning is to be repeated on reordered versions of agiven problem. The problem is reordered automatically by CPLEX permuting its


rows and columns. This repetition is helpful when only one problem is beingtuned, as repeated reordering and re-tuning may lead to more robust tuningresults.

This parameter applies to only one problem in a tuning session. That is, in theInteractive Optimizer, this parameter is effective only when you are tuning a singleproblem; in the Callable Library (C API), this parameter is effective only when youare tuning a single problem with the routine CPXtuneparam .

Values

Any nonnegative integer; default: 1 (one)

tuning time limit in secondsSets a nondeterministic time limit in seconds per model and per test set (that is,suite of models) applicable in tuning.

Purpose

Nondeterministic tuning time limit (wall-clock time)C Name CPX_PARAM_TUNINGTILIM (double)

C++ Name TuningTiLim (double)

Java Name TuningTiLim (double)

.NET Name TuningTiLim (double)

OPL Name tuningtilim

Python Name tuning.timelimit

MATLAB Name tuning.timelimit

Interactive Optimizer tune timelimit

Identifier 1112

Description

Sets a nondeterministic time limit in seconds per model and per test set (that is,suite of models) applicable in tuning. This parameter is also known as thewall-clock time limit on tuning.

Interaction with other parameters: deterministic tuning time limit

The “deterministic tuning time limit” on page 134(CPX_PARAM_TUNINGDETTILIM, TuningDetTiLim) is not compatible with thiswall-clock, nondeterministic tuning time limit (CPX_PARAM_TUNINGTILIM,TuningTiLim). Only one of these two parameters can be set to a finite value at atime. Any attempt to set either of these parameters to a finite value while the otheris already set to a finite value results in the errorCPXERR_PARAM_INCOMPATIBLE from the routine CPXsetdblparam or themethod setDblParam (depending on your choice of API).

Finite values of tuning time limits

If this wall-clock, nondeterministic tuning time parameter(CPX_PARAM_TUNINGTILIM, TuningTiLim) is set to a finite value, then thetuning process itself is nondeterministic, and CPLEX recommends appropriateparameter settings to minimize the wall-clock optimization time.


If the “deterministic tuning time limit” on page 134(CPX_PARAM_TUNINGDETTILIM, TuningDetTiLim) is set to a finite value, thenthe tuning process itself is deterministic, and CPLEX recommends appropriateparameter settings to minimize the deterministic optimization time.

The default value of this parameter is 1e+75 (effectively, infinite).

Likewise, the default value of the “deterministic tuning time limit” on page 134(CPX_PARAM_TUNINGDETTILIM, TuningDetTiLim) is also 1e+75 (effectively,infinite).

If this parameter is set at its default value 1e+75, and if the “deterministic tuningtime limit” on page 134 (CPX_PARAM_TUNINGDETTILIM, TuningDetTiLim) isalso set at its default value 1e+75, then the combination is equivalent to setting thedeterministic tuning time limit to 10 000 000 ticks. Consequently, these combineddefault settings make the tuning process deterministic, and CPLEX recommendssettings to minimize the deterministic optimization time.

Unlimited time per model

If you want to run a tuning session with unlimited time per model, then set one ofthe tuning time limit parameters (either wall-clock “tuning time limit in seconds”on page 138 (CPX_PARAM_TUNINGTILIM, TuningTiLim) or “deterministic tuningtime limit” on page 134 (CPX_PARAM_TUNINGDETTILIM, TuningDetTiLim) to avery large value that is strictly less than 1e+75 (for example, 1e+74). If you setCPX_PARAM_TUNINGDETTILIM, TuningDetTiLim to a finite value, then thetuning process will be deterministic. If you set CPX_PARAM_TUNINGTILIM,TuningTiLim to a finite value, then the tuning process will be nondeterministic.

Examples

For an example of how to use general and tuning-specific time limit parameterstogether, see Examples: time limits on tuning in the Interactive Optimizer in theCPLEX User’s Manual.

Values

Any nonnegative number; default: 1e+75 seconds.

See also


“deterministic time limit” on page 44

“deterministic tuning time limit” on page 134

MIP variable selection strategySets the rule for selecting the branching variable at the node which has beenselected for branching.

Purpose

MIP variable selection strategyC Name CPX_PARAM_VARSEL (int)


C++ Name VarSel (int)

Java Name VarSel (int)

.NET Name VarSel (int)

OPL Name varsel

Python Name mip.strategy.variableselect

MATLAB Name mip.strategy.variableselect in Cplex Class API

MATLAB Name mip.strategy.variableselect in Toolbox (CPLEX compatible)

MATLAB Name BranchStrategy in Toolbox (Optimization Toolbox compatible)

Interactive Optimizer mip strategy variableselect

Identifier 2028

Description

Sets the rule for selecting the branching variable at the node which has beenselected for branching.

The minimum infeasibility rule chooses the variable with the value closest to aninteger but still fractional. The minimum infeasibility rule (-1) may lead morequickly to a first integer feasible solution, but is usually slower overall to reach theoptimal integer solution.

The maximum infeasibility rule chooses the variable with the value furtherest froman integer. The maximum infeasibility rule (1 one) forces larger changes earlier inthe tree.

Pseudo cost (2) variable selection is derived from pseudo-shadow prices.

Strong branching (3) causes variable selection based on partially solving a numberof subproblems with tentative branches to see which branch is the most promising.This strategy can be effective on large, difficult MIP problems.

Pseudo reduced costs (4) are a computationally less-intensive form of pseudo costs.

The default value (0 zero) allows CPLEX to select the best rule based on theproblem and its progress.

Table 78. Values


-1 CPX_VARSEL_MININFEAS Branch on variable with minimuminfeasibility

0 CPX_VARSEL_DEFAULT Automatic: let CPLEX choose variable tobranch on; default

1 CPX_VARSEL_MAXINFEAS Branch on variable with maximuminfeasibility

2 CPX_VARSEL_PSEUDO Branch based on pseudo costs

3 CPX_VARSEL_STRONG Strong branching

4 CPX_VARSEL_PSEUDOREDUCED Branch based on pseudo reduced costs

directory for working filesSpecifies the name of an existing directory into which CPLEX may store temporaryworking files.


Purpose

Directory for working filesC Name CPX_PARAM_WORKDIR (string)

C++ Name WorkDir (string)

Java Name WorkDir (string)

.NET Name WorkDir (string)

OPL Name workdir

Python Name workdir

MATLAB Name workdir

Interactive Optimizer workdir

Identifier 1064

Description

Specifies the name of an existing directory into which CPLEX may store temporaryworking files, such as for MIP node files or for out-of-core barrier files. The defaultis the current working directory.

This parameter accepts a string as its value. If you change either the “API stringencoding switch” on page 20 or the “file encoding switch” on page 56 from theirdefault value to a multi-byte encoding where a NULL byte can occur within theencoding of a character, you must take into account the issues documented in thetopic Selecting an encoding in the CPLEX User's Manual. Especially consider thepossibility that a NULL byte occurring in the encoding of a character caninadvertently signal the termination of a string, such as a filename or directorypath, and thus provoke surprising or incorrect results.

Values

Any existing directory; default: ‘.’

memory available for working storageSpecifies an upper limit on the amount of central memory, in megabytes, thatCPLEX is permitted to use for working memory.

Purpose

Memory available for working storageC Name CPX_PARAM_WORKMEM (double)

C++ Name WorkMem (double)

Java Name WorkMem (double)

.NET Name WorkMem (double)

OPL Name workmem

Python Name workmem

MATLAB Name workmem

Interactive Optimizer workmem

Identifier 1065


Description

Specifies an upper limit on the amount of central memory, in megabytes, thatCPLEX is permitted to use for working memory before swapping to disk files,compressing memory, or taking other actions.

Values

Any nonnegative number, in megabytes; default: 128.0

See also


write level for MST, SOL filesSets a level of detail for CPLEX to write a file in MST or SOL format.

Purpose

Write level for MST, SOL filesC Name CPX_PARAM_WRITELEVEL (int)

C++ Name WriteLevel (int)

Java Name WriteLevel (int)

.NET Name WriteLevel (int)

Python Name output.writelevel

MATLAB Name output.writelevel

Interactive Optimizer output writelevel

Identifier

Description

Sets the level of detail for CPLEX to write a solution to a file in SOL format or aMIP start to a file in MST format. CPLEX writes information about a MIP start to aformatted file of type MST with the file extension .mst. CPLEX writes informationabout a solution to a formatted file of type SOL with the file extension .sol.CPLEX records the write level at which it created a file in that file, so that the filecan be read back accurately later.

The default setting of this parameter is 0 (zero) AUTO; that is, let CPLEX decidethe level of detail. CPLEX behaves differently, depending on whether the format isSOL or MST and on whether it is writing a solution or MIP start. For SOL files,AUTO resembles level 1 (one): CPLEX writes all variables and their respectivevalues to the file. For MST files, AUTO resembles level 2: CPLEX writes discretevariables and their respective values to the file.

When the value of this parameter is 1 (one), CPLEX writes all variables, bothdiscrete and continuous, with their values.

When the value of this parameter is 2, CPLEX writes values for discrete variablesonly.

When the value of this parameter is 3, CPLEX writes values of nonzero variablesonly.


When the value of this parameter is 4, CPLEX writes values of nonzero discretevariables only.

Treatment of nonzeros

With respect to levels 3 and 4, where nonzero values are significant, CPLEXconsiders a value nonzero if the absolute value is strictly less than 1e-16. In thecase of SOL files, CPLEX applies this test to primal and dual variable values, thatis, both x and pi variable values. In the case of MST files, CPLEX applies this testonly to x values.

Restrictions due to reduced file size

Levels 3 and 4 reduce the size of files, of course. However, this reduced file entailsrestrictions and may create surprising results when the file is re-used. Levels 3 and4 are not equivalent to levels 1 and 2. Indeed, if a MIP start does not contain avalue for a variable expected at level 3 or 4, then this variable will be fixed to 0(zero) when that MIP start file is processed. Specifically, at level 3, if the MIP startdoes not specify a value for a variable of any type, or at level 4, if the MIP startdoes not specify a value for a discrete variable, such a variable will be fixed to 0(zero). Consequently, the same MIP start written at level 1 or 2 may producesatisfactory solutions, but the reduced MIP start file, written at level 3 or 4,perhaps does not lead to solutions. This surprising situation arises typically in thecase of model changes with the addition of new variables.

Table 79. Values


0 AUTO Automatic: let CPLEX decide

1 CPX_WRITELEVEL_ALLVARS CPLEX writes all variablesand their values

2 CPX_WRITELEVEL_DISCRETEVARS CPLEX writes only discretevariables and their values

3 CPX_WRITELEVEL_NONZEROVARS CPLEX writes only nonzerovariables and their values

4 CPX_WRITELEVEL_NONZERODISCRETEVARS CPLEX writes only nonzerodiscrete variables and theirvalues

MIP zero-half cuts switchDecides whether or not to generate zero-half cuts for the problem.

Purpose

MIP zero-half cuts switchC Name CPX_PARAM_ZEROHALFCUTS (int)

C++ Name ZeroHalfCuts (int)

Java Name ZeroHalfCuts (int)

.NET Name ZeroHalfCuts (int)

OPL Name zerohalfcuts

Python Name mip.cuts.zerohalfcut

MATLAB Name mip.cuts.zerohalfcut


Interactive Optimizer mip cuts zerohalfcut

Identifier 2111

Description

Decides whether or not to generate zero-half cuts for the problem. The value0 (zero), the default, specifies that the attempt to generate zero-half cuts shouldcontinue only if it seems to be helping.

If you find that too much time is spent generating zero-half cuts for your model,consider setting this parameter to -1 (minus one) to turn off zero-half cuts.

If the dual bound of your model does not make sufficient progress, considersetting this parameter to 2 to generate zero-half cuts more aggressively.

For a definition of a zero-half cut, see the topic Zero-half cuts in the general topicCuts in the CPLEX User’s Manual. The table Parameters for controlling cutsru theEND, also in the user’s manual, includes links to the documentation of otherparameters affecting other types of cuts.

Table 80. Values

Value Meaning

-1 Do not generate zero-half cuts


1 Generate zero-half cuts moderately

2 Generate zero-half cuts aggressively


Index

Aabsolute gap

solution pool 122absolute objective difference 88accessing

dual values of QCP 33parameters 1sets of parameters 1

advanced start 17barrier and 17basis and 17node exploration limit 129presolve and 17repair tries 111root algorithm and 113

AdvInd 17AggCutLim 18AggFill 18aggregation limit 18APIEncoding 20AuxRootThreads 116

Bbacktracking

criteria for 32node selection and 86tolerance 32

BarAlg 22BarColNz 23BarCrossAlg 24BarDisplay 24BarEpComp 25BarGrowth 26BarItLim 26BarMaxCor 27BarObjRng 27BarOrder 28BarQCPEpComp 28barrier

advanced start and 17detecting unbounded optimal

faces 26maximum absolute objective

function 27barrier limit

absolute value of objectivefunction 27

centering corrections 27detecting unbounded optimal

faces 26growth 26iterations 26

BarStartAlg 29basic variable

feasitility tolerance and 54basis

advanced start and 17crash ordering and 39kappa computation 73

basis (continued)Markowitz threshold and 51network feasibility tolerance and 81optimal and feasibility tolerance 54root algorithm and 113simplex iterations and 65simplex refactoring frequency

and 109singularity repairs and 121

BBInterval 30best bound interval 30best node

absolutee mip gap and 48backtracking and 32relative MIP gap and 49target gap and 32

BndStrenInd 30bound strengthening 30bound violation

feasibility (simplex) 54FeasOpt 55network flow 81

branch direction 31branching, local 66BranchStrategy 139BrDir 31BtTol 32

CCalcQCPDuals 33callback reduced LP parameter 69callback, control 76callbacks

parallelism and 131threads and 131

candidate list limit (MIP) 128centering correction 27clique cut 34Cliques 34ClockType 35CloneLog 35CoeRedInd 36ColReadLim 37complementarity convergence

barrier (LP, QP) 25barrier (QCP) 28LP 25QCP 28QP 25

condition number 73ConflictDisplay 38control callback 76cover cut 38cover cut, flow 57Covers 38CPX_PARAM_ADVIND 17CPX_PARAM_AGGCUTLIM 18CPX_PARAM_AGGFILL 18CPX_PARAM_AGGIND 19CPX_PARAM_APIENCODING 20

CPX_PARAM_AUXROOTTHREADS 116CPX_PARAM_BARALG 22CPX_PARAM_BARCOLNZ 23CPX_PARAM_BARCROSSALG 24CPX_PARAM_BARDISPLAY 24CPX_PARAM_BAREPCOMP 25CPX_PARAM_BARGROWTH 26CPX_PARAM_BARITLIM 26CPX_PARAM_BARMAXCOR 27CPX_PARAM_BAROBJRNG 27CPX_PARAM_BARORDER 28CPX_PARAM_BARQCPEPCOMP 28CPX_PARAM_BARSTARTALG 29CPX_PARAM_BBINTERVAL 30CPX_PARAM_BNDSTRENIND 30CPX_PARAM_BRDIR 31CPX_PARAM_BTTOL 32CPX_PARAM_CALCQCPDUALS 33CPX_PARAM_CLIQUES 34CPX_PARAM_CLOCKTYPE 35CPX_PARAM_CLONELOG 35CPX_PARAM_COEREDIND 36CPX_PARAM_COLREADLIM 37CPX_PARAM_CONFLICTDISPLAY 38CPX_PARAM_COVERS 38CPX_PARAM_CRAIND 39CPX_PARAM_CUTLO 40CPX_PARAM_CUTPASS 41CPX_PARAM_CUTSFACTOR 41CPX_PARAM_CUTUP 42CPX_PARAM_DATACHECK 43CPX_PARAM_DEPIND 43CPX_PARAM_DETTILIM 44CPX_PARAM_DISJCUTS 45CPX_PARAM_DIVETYPE 46CPX_PARAM_DPRIIND 47CPX_PARAM_EACHCUTLIM 47CPX_PARAM_EPAGAP 48CPX_PARAM_EPGAP 49CPX_PARAM_EPINT 50CPX_PARAM_EPMRK 51CPX_PARAM_EPOPT 52CPX_PARAM_EPPER 53CPX_PARAM_EPRELAX 53CPX_PARAM_EPRHS 54CPX_PARAM_FEASOPTMODE 55CPX_PARAM_FILEENCODING 56CPX_PARAM_FLOWCOVERS 57CPX_PARAM_FLOWPATHS 58CPX_PARAM_FPHEUR 59CPX_PARAM_FRACCAND 60CPX_PARAM_FRACCUTS 60CPX_PARAM_FRACPASS 61CPX_PARAM_GUBCOVERS 61CPX_PARAM_HEURFREQ 62CPX_PARAM_IMPLBD 63CPX_PARAM_INTSOLFILEPREFIX 64CPX_PARAM_INTSOLLIM 65CPX_PARAM_ITLIM 65CPX_PARAM_LANDPCUTS 67CPX_PARAM_LBHEUR 66


CPX_PARAM_LPMETHOD 113CPX_PARAM_MCFCUTS 67CPX_PARAM_MEMORYEMPHASIS 68CPX_PARAM_MIPCBREDLP 69CPX_PARAM_MIPDISPLAY 70CPX_PARAM_MIPEMPHASIS 71CPX_PARAM_MIPINTERVAL 72CPX_PARAM_MIPKAPPASTATS 73CPX_PARAM_MIPORDIND 75CPX_PARAM_MIPORDTYPE 75CPX_PARAM_MIPSEARCH 76CPX_PARAM_MIQCPSTRAT 77CPX_PARAM_MIRCUTS 78CPX_PARAM_MPSLONGNUM 79CPX_PARAM_NETDISPLAY 80CPX_PARAM_NETEPOPT 80CPX_PARAM_NETEPRHS 81CPX_PARAM_NETFIND 81CPX_PARAM_NETITLIM 82CPX_PARAM_NETPPRIIND 82CPX_PARAM_NODEFILEIND 84CPX_PARAM_NODELIM 85CPX_PARAM_NODESEL 86CPX_PARAM_NUMERICALEMPHASIS 86CPX_PARAM_NZREADLIM 87CPX_PARAM_OBJDIF 88CPX_PARAM_OBJLLIM 88CPX_PARAM_OBJULIM 89CPX_PARAM_PARALLELMODE 90CPX_PARAM_PERIND 92CPX_PARAM_PERLIM 92CPX_PARAM_POLISHAFTERDETTIME 93CPX_PARAM_POLISHAFTEREPAGAP 94CPX_PARAM_POLISHAFTEREPGAP 94CPX_PARAM_POLISHAFTERINTSOL 95CPX_PARAM_POLISHAFTERNODE 96CPX_PARAM_POLISHAFTERTIME 97CPX_PARAM_POLISHTIME

(deprecated) 98CPX_PARAM_POPULATELIM 98CPX_PARAM_PPRIIND 99CPX_PARAM_PREDUAL 100CPX_PARAM_PREIND 101CPX_PARAM_PRELINEAR 102CPX_PARAM_PREPASS 102CPX_PARAM_PRESLVND 103CPX_PARAM_PRICELIM 104CPX_PARAM_PROBE 104CPX_PARAM_PROBEDETTIME 105CPX_PARAM_PROBETIME 106CPX_PARAM_QPMAKEPSDIND 106CPX_PARAM_QPMETHOD 114CPX_PARAM_QPNZREADLIM 107CPX_PARAM_RANDOMSEED 107CPX_PARAM_REDUCE 108CPX_PARAM_REINV 109CPX_PARAM_RELAXPREIND 109CPX_PARAM_RELOBJDIF 110CPX_PARAM_REPAIRTRIES 111CPX_PARAM_REPEATPRESOLVE 111CPX_PARAM_RINSHEUR 112CPX_PARAM_ROWREADLIM 117CPX_PARAM_SCAIND 118CPX_PARAM_SCRIND 118CPX_PARAM_SIFTALG 119CPX_PARAM_SIFTDISPLAY 120CPX_PARAM_SIFTITLIM 120

CPX_PARAM_SIMDISPLAY 121CPX_PARAM_SINGLIM 121CPX_PARAM_SOLNPOOLAGAP 122CPX_PARAM_SOLNPOOLCAPACITY 123CPX_PARAM_SOLNPOOLGAP 124CPX_PARAM_SOLNPOOLINTENSITY 125CPX_PARAM_SOLNPOOLREPLACE 126CPX_PARAM_SOLUTIONTARGET 127CPX_PARAM_STARTALG 115CPX_PARAM_STRONGCANDLIM 128CPX_PARAM_STRONGITLIM 129CPX_PARAM_SUBALG 83CPX_PARAM_SUBMIPNODELIM 129CPX_PARAM_SYMMETRY 130CPX_PARAM_THREADS 131CPX_PARAM_TILIM 132CPX_PARAM_TRELIM 133CPX_PARAM_TUNINGDETTILIM 134CPX_PARAM_TUNINGDISPLAY 136CPX_PARAM_TUNINGMEASURE 137CPX_PARAM_TUNINGREPEAT 137CPX_PARAM_TUNINGTILIM 138CPX_PARAM_VARSEL 139CPX_PARAM_WORKDIR 141CPX_PARAM_WORKMEM 141CPX_PARAM_WRITELEVEL 142CPX_PARAM_ZEROHALFCUTS 143CraInd 39cut

cliques (MIP) 34constraint aggregation limit and 18covers (MIP) 38disjunctive (MIP) 45flow cover 57flow path (MIP) 58fractional pass limit 61Gomory fractional candidate limit 60Gomory fractional generation 60GUB (MIP) 61implied bound 63lift and project (MIP) 67limit by type 47limiting number of 41MIP display and 70mixed integer rounding (MIR) 78node limit and 85pass limit 41reapplying presolve and 111user-defined and preprocessing 102zero-half 143

CutLo 40cutoff tolerance 32CutPass 41CutsFactor 41CutUp 42

DDataCheck 43DepInd 43deterministic

definition 90deterministic tick (definition) 44deterministic time

and solution polishing 93DetTiLim 44Diagnostics 118

DisjCuts 45disjunctive cut 45DisplayFunc 118DiveType 46DPriInd 47dual value

calculating for QCP 33

EEachCutLim 47EpAGap 48EpGap 49EpInt 50EpLin 50EpMrk 51EpOpt 52EpPer 53EpRelax 53EpRHS 54

FFeasOpt

lower objective limit 53mode 55

FeasOptMode 55FileEncoding 56first order optimality conditions 127flow cover cut 57

aggregation limit 18flow path cut 58FlowCovers 57FlowPaths 58FPHeur 59FracCand 60FracCuts 60FracPass 61fractional cut

candidate limit 60generation 60pass limit 61

GGomory fractional cut

candidate limit 60generation 60pass limit 61

GUB cut 61GUBCovers 61

HHeurFreq 62heuristic

frequency 62local branching 66relaxation induced neighborhood

search (RINS) 112

IImplBd 63implied bound cut 63


incumbentbacktracking and 32cutoff tolerance and 32diving and 46local branching heuristic and 66relaxation induced neighborhood

search (RINS) and 112solution pool absolute gap and 122solution pool relative gap and 124target gap and 32

integer solutiondiving and 46

integer solution file prefix 64integer solution limit 65IntSolFilePrefix 64IntSolLim 65iteration

barrier centering corrections and 27iteration limit

barrier 26network 82perturbation and (simplex) 92refactoring of basis (simplex)

and 109sifting 120simplex 65strong branching and (MIP) 129

ItLim 65

Kkappa 73

Llazy constraint

nonlinear reductions and 102preprocessing and 102, 108presolve reductions and 108

LBHeur 66lift-and-project cut 67LiftProjCuts 67local branching heuristic 66

MMarkowitz tolerance 51maximum infeasibility rule

variable selection and 139MaxIter 65MaxNodes 85MaxTime 132MCFCuts 67MemoryEmphasis 68minimum infeasibility rule

variable selection and 139MIP

bound strengthening 30kappa computation 73solution file name 64solution file prefix 64writing solutions to file 64

MIP callback reduced LP parameter 69MIP limit

aggregation for cuts 18cut by type 47

MIP limit (continued)cuts 41cutting plane passes 41deterministic probing time 105Gomory fractional cut candidates 60nodes explored in subproblem 129passes for Gomory fractional cuts 61polishing time (deprecated) 98probing time 106repair tries 111size of tree 133solutions 65termination criterion 85

MIP performancenumerical difficulties 73

MIP startwriting to file 142

MIP strategybacktracking 32best bound interval 30branch direction 31branching variable 139diving 46heuristic frequency 62local branching 66node algorithm 83node file management 84node selection 86presolve at nodes 103priority order 75probing 104quadratically constrained programs

(MIQCP) 77RINS 112root algorithm 115strong branching and candidate

limit 128strong branching and iteration

limit 129MIP tree

advanced start and 17MIPDisplay 70MIPEmphasis 71MIPInterval 72MIPKappaStats 73MIPOrdInd 75MIPOrdType 75MIPSearch 76MIQCPStrat 77MIR cut 78

aggregation limit 18MIRCuts 78mixed integer programming (MIP)

threads 131mixed integer rounding cut 78MPSLongNum 79multi-commodity flow cut 67

NNetDisplay 80NetEpOpt 80NetEpRHS 81NetFind 81NetItLim 82NetPPriInd 82network with arc capacity 67

nodebest estimate 30presolve and 103

node filecompression of 84

node relaxation in MIQCP strategy 77node selection

backtracking and 86best bound interval and 30

NodeAlg 83NodeDisplayInterval 72NodeFileInd 84NodeLim 85NodeSearchStrategy 86NodeSel 86nonconvex continuous QP solution

type 127nonconvex MIQP solution type 127numerical emphasis 73NumericalEmphasis 86NzReadLim 87

OObjDif 88objective

current and backtracking 32objective difference

absolute 88relative 110

ObjLLim 88ObjULim 89opportunistic

definition 90optimality tolerance (simplex) 52

Pparallel optimization

cloning log files for 35parallelism

callbacks and 131optimization mode 90threads and 131

ParallelMode 90parameter set 1path cut 58PerInd 92periodic heuristic 62PerLim 92perturbation constant (simplex) 53pivot selection 51PolishAfterDetTime 93PolishAfterEpAGap 94PolishAfterEpGap 94PolishAfterIntSol 95PolishAfterNode 96PolishAfterTime 97PolishTime (deprecated) 98PopulateLim 98PPriInd 99PreDual 100PreInd 101PreLinear 102PrePass 102

Index 147

preprocessinglazy constraints and 102, 108

PreslvNd 103presolve

advanced start and 17nodes and 103

PriceLim 104pricing

candidate list limit 104network 82types available for dual simplex 47types available in primal simplex 99

priority orderindicator 75type to generate 75

Probe 104ProbeDetTime 105ProbeTime 106probing

deterministic time limit 105MIP branching and 104time limit 106

pseudo costvariable selection and 139

pseudo reduced costvariable selection and 139

pseudo-shadow pricevariable selection and 139

QQPmakePSDInd 106QPNzReadLim 107quadratically constrained mixed integer

program (MIQCP) 77

Rrandom seed 107RandomSeed 107Reduce 108ReInv 109relative gap

solution pool 124relative objective difference 110relaxation induced neighborhood search

(RINS) 112RelaxPreInd 109RelObjDif 110RepairTries 111RepeatPresolve 111RINSHeur 112root

threads parameter 116RootAlg 113, 114, 115RowReadLim 117

SScaInd 118screen indicator 118screen indicator not available in this

API 118set of parameters 1SiftAlg 119SiftDisplay 120

siftingiteration limit 120node algorithm as 83root algorithm as 113

SiftItLim 120SimDisplay 121simplex

iterations and candidate list 129perturbation constant 53

simplex limitdegenerate iterations 92iterations 65lower objective function 88repairs of singularities 121upper objective function 89

SingLim 121singularity 121SolnPoolAGap 122SolnPoolCapacity 123SolnPoolGap 124SolnPoolIntensity 125SolnPoolReplace 126solution

writing to file 142solution polishing

absolute gap as starting conditionfor 94

deterministic time as startingcondition for 93

integer solutions as starting conditionfor 95

nodes processed as starting conditionfor 96

relative gap as starting conditionfor 94

time as starting condition for 97solution pool

absolute gap 122capacity 123intensity 125populate limit 98relative gap 124replacement strategy 126

SolutionTarget 127start, advanced 17strong branching

candidate list and 128iteration limit and 129variable selection and 139

StrongCandLim 128StrongItLim 129SubMIPNodeLim 129Symmetry 130

Ttarget gap 32termination criterion

barrier complementarity convergence(LP, QP) 25

barrier complementarity convergence(QCP) 28

barrier iterations 26FeasOpt Phase I 53MIP node limit 85network iteration limit 82simplex iteration limit 65

termination criterion (continued)tree size (MIP) 133tree size and memory 84

threadscallbacks and 131count at root 116parallelism and 131

Threads 131tick (definition) 44TiLim 132tilting 36time

as starting condition for solutionpolishing 97

as starting condition for solutionpolishing, deterministic 93

deterministic and solutionpolishing 93

time limit 44in seconds 132

toleranceabsolute MIP gap 48absolute MIP objective difference 88backtracking (MIP) 32barrier complementarity convergence

(LP, QP) 25basic variables and bound

violation 54complementarity convergence

QCP 28cutoff 32cutoff and backtracking 32feasibility (network primal) 81FeasOpt relaxation 53linearization 50lower cutoff 40Markowitz 51MIP integrality 50optimality (network) 80optimality (simplex) 52relative MIP gap 49relative MIP objective difference 110solution pool, absolute 122solution pool, relative 124upper cutoff 42

treememory limit (MIP) 133MIP advanced start 17

TreLim 133tuning

deterministic time limit 134measure 137repetition of 137reporting level 136time limit 138wall-clock time limit 138

TuningDetTiLim 134TuningDisplay 136TuningMeasure 137TuningRepeat 137TuningTiLim 138

Uunbounded optimal face 26


Vvariable selection

candidate list and 128MIP strategy 139simplex iterations and 129

variable, basicfeasibility tolerance and 54

VarSel 139

WWorkDir 141working directory

node files and 84temporary files and 141

working memorylimit on 141node files and 84

WorkMem 141WriteLevel 142writing

MIP solutions to file (parameter) 64

Zzero-half cuts 143ZeroHalfCuts 143

Index 149


��

Printed in USA

Date post:	27-Feb-2022
Category:	Documents
Upload:	others
View:	12 times
Download:	0 times

CPLEX Parameters Reference - IBM

Documents