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Table of Contents
                            Linear Programming with MATLAB
	MPS-SIAM Series on Optimization
	ISBN 978-0-898716-43-6
	Contents
	Preface
	Chapter 1 Introduction
	Chapter 2 Linear Algebra: A Constructive Approach
	Chapter 3 The Simplex Method
	Chapter 4 Duality
	Chapter 5 Solving Large Linear Programs
	Chapter 6 Sensitivity and Parametric Linear Programming
	Chapter 7 Quadratic Programming and Complementarity Problems
	Chapter 8 Interior-Point Methods
	Chapter 9 Approximation and Classification
	Appendix A Linear Algebra, Convexity, and Nonlinear Functions
	Appendix B Summary of Available MATLAB Commands
	Bibliography
	Index
                        
Document Text Contents
Page 2

LINEAR PROGRAMMING

WITH MATLAB

MP07_Ferris_FMA.qxp 10/4/2007 2:22 PM Page 1

Page 139

126 Chapter 5. Solving Large Linear Programs

tableau. Since A·B = LU and A′·Bu = pB, it follows that u = (L′)−1(U ′)−1pB. Hence,
we can reuse the L and U factors of A·B calculated above (leading to important savings in
practice) and proceed in MATLAB as follows:

� u = L’\(U’\p(B));
� c = p(N)-A(:,N)’*u

c =

−1−3

3




Since c′ makes up the bottom row of the tableau (except for the bottom-right element) and
since it has negative entries, the current tableau is not optimal. Therefore, we select an
index to enter the basis, corresponding to one of the negative entries in c, and calculate the
column in the tableau that corresponds to this variable xN(s).

� s = 2;
� d = U\(L\A(:,N(s)))

d =

 46

14




We now carry out the ratio test with the values of d and xB calculated above and find that
r = 1 is the index that leaves the basis. We swap r and s between the sets B and N and
continue with the next iteration of the revised simplex method.

� r=1; swap = B(r);
� B(r) = N(s); N(s) = swap;
� [L,U] = lu(A(:,B));
� x_B = U\(L\b)
� u = L’\(U’\p(B));
� c = p(N)-A(:,N)’*u

c =

−0.250.75

2.25




Now c has the value shown above. It indicates that we have not yet achieved optimality, as
there is still a negative component. We identify the component s = 1 to enter the basis and
a component r = 1 to leave, and we continue with the next simplex iteration.

� s = 1; d = U\(L\A(:,N(s)))
� r = 1; swap = B(r);
� B(r) = N(s); N(s) = swap;
� [L,U] = lu(A(:,B));
� x_B = U\(L\b)
� u = L’\(U’\p(B));
� c = p(N)-A(:,N)’*u

c =

11

2




Page 140

5.2. The Revised Simplex Method 127

Since we now have c ≥ 0, the current tableau is optimal. The corresponding solution is
x = (1, 0, 0, 2, 0, 3)′, with z = p′x = p′

B
xB = 2.

Exercise 5-2-2. In MATLAB, solve the following linear program using the revised simplex
method as shown above. You will need to convert the data of this problem to canonical
form and choose an initial B that corresponds to the slack variables that you add during this
conversion.

max −x1 + 2x2 + x3
subject to x1 + x2 + x3 ≤ 3,

−x1 + x2 − x3 ≤ 1,
2x1 + x2 − x3 ≤ 1,
−x1 + x2 ≤ 4,

x1, x2, x3 ≥ 0.
You should work through each step of the method explicitly, calculating each of the inter-
mediate vectors, as in the example above.

In fact, it is computationally more efficient to update the LU factorization at each step
of the revised simplex method instead of recomputing the factorization anew. Details on
such procedures can be found in Section 5.2.3; in the absence of these methods the overall
complexity of the revised simplex method will be larger than that of an implementation
using Jordan exchanges.

Numerical rounding error causes many problems for implementations of the simplex
method due to the special nature of the number zero and the need to determine positivity
or negativity of components in crucial steps of the procedure. Typical commercial codes
contain at least three types of “zero tolerances,” small positive numbers below which a
floating-point number is deemed indistinguishable from zero for purposes of the algorithm.
The first tolerance zer_tol is used in comparisons of numbers against zero. In testing
the bottom row of the tableau for negativity, we use the MATLAB code

if isempty(find(c < -zer_tol))

rather than the simple test

if isempty(find(c < 0))

This allows some of the values of c to be slightly negative. Chvátal (1983) suggests a value
of 10−5 as a typical value for this tolerance.

The second tolerance is a pivot tolerance piv_tol that is used to safeguard against
very small pivot elements and hence avoid the problems shown in Example 2-6-1. Pivots are
deemed acceptable only if the potential pivot element is greater than piv_tol in absolute
value. A typical value for this tolerance is 10−8.

The third zero tolerance is a slack tolerance slack_tol that is a measure of how
much error we will allow in the slack variables, that is, the difference between A BxB and b.
The use of this tolerance will be outlined further in the sections on advanced pivot selection
mechanisms and basis updates. A typical value for slack_tol is 10−6.

A simple version of the resulting revised simplex method that uses these tolerances
and LU decomposition for solving the systems of linear equations is given in rsm.m. We
illustrate the use of this routine on the example given above.

Page 278

Index 265

pivot, 18, 20, 46, 47. See also Jordan exchange
block, 31
degenerate, 62, 64, 69
nondegenerate, 67
submatrix, 31

pivot selection, 15. See also pricing
Devex, 142, 143
for dual simplex, 103
for Lemke’s algorithm, 179
steepest-edge, 142

polynomial complexity, 207
predictor-corrector method.

See path-following methods
preprocessing, 212
pricing, 47, 49, 53, 68, 117

partial, 142
primal-dual methods. See interior-point

methods
primal-dual pair of linear programs, 89

definition, 94
for general linear programs, 108
interior-point methods and, 195
optimality conditions for, 100
weak duality for, 97
zero-sum games and, 192

prisoners’ dilemma, 186, 191
projection, 173, 205, 244
pure strategies, 185

QR factorization, 226
quadratic function, 244–247

convex, 244–247
quadratic programming, 7, 172–177,

212–215, 227, 231
convex, 172, 173, 175, 212
general form, 175
infeasible, 182, 183
nonconvex, 182, 184
unbounded, 182, 183

rank, 91, 225
determination via Jordan exchanges,

91–92
full, 225

ratio test, 48, 126, 203
after addition of constraints, 157

definition, 49
for dual simplex method, 103, 104,

157, 165
in Lemke’s method, 179, 180, 188
robust implementation of, 143
ties in, 66, 68

reduced costs, 49, 68, 124, 132, 142, 152,
153, 160

regression problems. See approximation
problems

residual vector, 219, 221, 227
outliers, 227

resource allocation, 10–11
revised simplex method, 52, 117, 123–143,

156
choice of initial basis, 125
for problems with bound constraints,

129, 134
fundamental idea, 123–124
initial basic feasible solution, 134–139
linear algebra in, 124–126
pivot selection, 142–143
roundoff error issues, 127–129
specification, 124

roundoff error, 28, 127, 143

saddle point, 184
scalar product, 252
Scheme II for linear programs in nonstan-

dard form, 72, 80–86, 157, 220,
221, 223

Schur complement, 31
sensitivity analysis, 4, 151
separating hyperplane, 11, 230, 231, 233
separation lemma, 113
shadow prices, 97, 154, 155. See also

Lagrange multipliers
Sherman–Morrison–Woodbury formula,

140
shifting, 74
simplex method, 6, 7, 45–87

introduction, 14–15
network, 149–150

slack variables, 8, 45, 117, 152, 196
definition, 45
dual, 101, 103

Page 279

266 Index

smallest-subscript rule, 67–72, 78, 104
solution set, 58–60
source, 145
standard form, 7, 16, 45, 117, 151
standard form, transformation to, 72–76

free variable method, 75
Scheme I, 72, 76–80
Scheme II, 80–86
substitution method, 74

Steinitz theorem, 26–27, 38, 92
support vector machines

linear, 230–233
linear programming (�1) formulation,

233
nonlinear, 233–234
nonseparable, 231–233
separable, 230–231

support vectors, 231, 232

tableau, 18–20
augmented, 62
condensed, 45, 47, 123
degenerate, 65, 66, 120
dual, 89
feasible, 46, 187
initial, 47, 119
labels, 21
MATLAB representation, 21–22
nondegenerate, 65
optimal, 49, 53, 58, 71, 152–154,

156
primal, 89
unbounded, 54

Taylor’s theorem, 170, 198, 199, 249

theorem of the alternative, 113
tolerance

pivot, 127
slack, 127

triangular substitution, 40, 139, 141, 209

unbounded linear program, 14, 46, 54–56,
66, 98

unbounded ray, 54, 182, 183
uncertainty in formulation, 151

variables, 2
artificial, 61, 62, 135, 167, 179, 218

for equality constraints, 80–82, 193
basic, 45, 152
blocking, 133
dependent, 18, 21, 23
dual, 89. See also Lagrange

multipliers
free, 75, 80, 107, 134, 136–139, 167,

184, 222
independent, 18, 21, 23, 26
nonbasic, 45, 51, 130
nonnegative, 107
slack. See slack variables

vector
norm. See norm
notation, 237
representation in MATLAB, 251

vertex, 14–15, 46, 51–53, 120, 163
adjacent, 15
degenerate, 52

zero-sum game, 185, 192–193

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