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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
N
Nm
Nm
NN
21
22
11
0101
,,,,,0
,0,
,0,
...')(
2
1tosubject
maximize
The basic mechanism of the simplex method: A simple example
0,0
4
3
2
1015)(
21
21
2
1
21
xx
xx
x
x
xxxZ
s.t.
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
N
MN
The basic mechanism of the simplex method: A simple example
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
0,0
4
3
2
1015)(
21
21
2
1
21
xx
xx
x
x
xxxZ
s.t.
maximize
The basic mechanism of the simplex method: A simple example
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
4
3
2
1015)(
32121
321
22
11
21
sssxx
sxx
sx
sx
xxxZ
s.t.
maximize
The basic mechanism of the simplex method: A simple example
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
21 x
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
s1
s2
s3
Slack variablesbasicnonbasic
=0
entering
leaving
Minimum Ratio Test
basic
basic
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
2
13
4
m
p n
1 32
49
8
7
5 6
(1,1)
(4,4)
(0.5,1.5)
(5,4)
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