Easy Optimization Problems, Relaxation, Local Processing for a small subset of variables

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Easy Optimization Problems,

Relaxation,Local Processing

for a small subset of variables

Different types of relaxation

Variable by variable relaxation – strict minimization

Changing a small subset of variables simultaneously – Window strict minimization relaxation

Stochastic relaxation – may increase the energy – should be followed by strict minimization

Easy to solve problems Quadratic functional with / without

linear equality constraintsSolve a linear system of equationsQuadratization of the functional: P=1, P>2Linearization of the constraints: P=2

Inequality constraints: active set method

Linear functional and linear constraintsLinearization of the quadratic functional

Linear programming

minimize/maximize a linear function

under equality/inequality linear constraints Standard form:

The region satisfying all the constraints is the feasible region and it is convex

MmmRxcbax

RAccxA

RAbbxA

xaxaxaxZ

,,,,,0

...')(

1tosubject

maximize

The basic mechanism of the simplex method: A simple example

1015)(

maximize

Linear programming (cont.)

The number of corner points is finite The global maximum is at the corner point in

which Z(x) is greater or equal to the value of Z at all adjacent corner points

The simplex method (Dantzig 1948) starts at a feasible corner point and visited a sequence of corner points until a maximum is obtained

#of iterations is almost always O(M) or O(N) whichever is larger, but can become exponential for pathological cases

Standard form without equality constraints Start at the origin: always a feasible corner point N=2 , M=3 at most 10 corner points, but only 5

are feasible at (x1,x2)=(0,0) the two last constraints intersect

1015)(

maximize

Add slack variables which transform inequality constraints to equality constraints

Start at the origin: (x1,x2,s1,s2,s3)=(0,0,2,3,4) , Z=0 move to another corner point by letting, say, x1 grow x1 may grow until it hits another corner point, in which a

different constraint holds => setting some si=0

0,0,0,0,0

1015)(

maximize

Divide all variables into two groups: basic/nonbasic At the origin: (x1,x2,s1,s2,s3)=(0,0,2,3,4)

Choose a nonbasic that maximizes Z: x1 , this is the entering basic variable

x1 is increased until it hits the constraint

There, x1=2 => s1=0 and this is the leaving basic variable

At (2,0): (x1,x2,s1,s2,s3)=(2,0,0,3,4), Z=30

Update the equations and continue until no further increase in Z is available

Automatic exchange of variables: Simplex Tableau

Simplex Tableau with inequality constraints

In proper form: Exactly 1 basic variable per equation The coefficient of each basic variable is 1 and this is

the only non zero entry in its column The RHS reveal the values of all basic variables The entering basic variable has the most negative

entry in the 0th row (for the objective Z) The leaving basic variable is the one that minimizes

RHS/coefficient of entering variable Set the pivot to 1 and use it to eliminate all other non

zeros in its column The maximum is achieved when the 0th row 0

s1 s2 s3

Slack variablesbasicnonbasic

entering

leaving

Minimum Ratio Test

Simplex Tableau with inequality (‘less than’), equality and ‘greater than’ constraints

If an equality constraints are involved, e.g., x1+x2=4 The origin is not feasible Add an artificial variable to each equality constraint:

x1+x2+t1=4

If a constraint is with ‘greater than’ sign: 3x1+2x2 16 The origin is not feasible Add a slack variable and an artificial variable to each

‘greater than’ constraint: 3x1+2x2-s1+t1=16 In order to find a starting feasible corner point for the

original LP, solve a Phase 1 LP in which the objective is to minimize the sum of all artificial variables:

minimize ti until all ti=0 => feasible for the original LP

Simplex Tableau in generalA general LP problem involves: N original variables L less than constraints E equality constraints G greater than constraints Add L+G slack variables Add E+G artificial variables To find a starting feasible corner point for LP,

solve a Phase 1 LP: minimize ti (sum of artificial variables)

INFEASIBILE: if at the end of Phase 1 ti>0

If ti=0 continue to solve the original LP

UNBOUNDED: an entering basic variable is unlimited

Linear programming

The Simplex method: small and large problems Interior point methods: very large problems (Karmarker 1984, polynomial-time algorithm) Within ML should not exceed 100 variables Many available software: MATLAB, numerical

recipes,… Adjust your problem to the used software Linearization of both the energy functional and

the constraints: the placement problem under pair wise non-overlap constraints

Exc#6: Window relaxation for the graph drawing problem

Consider the following window W of 3x3 squares containing the nodes m,n and p:

m is of size 1x1 located at (2,2);n is of size 0.8x0.8 located at (3.4,3.2);p is of size 0.5x0.5 located at (2.5,3).Find a correction to the locations of m,n and p such that the

quadratic energy is minimized subject to inequality constraint demands that the area of nodes at each square <= 0.3

(0.5,1.5)

a. Calculate the current amount of nodes’ area present in each of the 9 squares

b. Calculate amkx e: the change (per unit length) in the amount of nodes’ area induced by a small change in the x direction of node m to square k, k=1,…,9. Similarly calculate amky , ankx , anky , apkx and apky

c. Write the quadratic energy E as a function of the corrections to the variables in W

d. Calculate the current value of E e. Write the 9 inequalities constraints associated with each

squaref. Choose the active set of constraints and write the

Lagrangiang. Calculate the resulting system of equations and solve ith. Does the solution seem to be reasonable?i. Choose .25 of the solution, does E decrease at that point?j. Write the linear programming formulation

Exc#6: Window relaxation for the graph drawing problem

Given a graph which is initially drawn at Introduce a grid of mxm squares, each square of

size hx by hy

Pick a window W of squares Define by akix (akiy) the change in the total area in

the k’th square per small change in 1. How should akix (akiy) be calculated2. Write the quadratic energy minimization problem

under equidensity constraints in W3. Write the resulting linear system of equations4. Write a linear programming formulation

)~,~( yx

)~(~ ii yx

Easy Optimization Problems, Relaxation, Local Processing for a small subset of variables

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