Linear and Nonlinear Programming

by ;
Edition: 3rd
Format: Hardcover
Pub. Date: 2008-06-13
Publisher(s): Springer Nature
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Summary

Linear and Nonlinear Programming is considered a classic textbook in Optimization. While it is a classic, it also reflects modern theoretical insights. These insights provide structure to what might otherwise be simply a collection of techniques and results, and this is valuable both as a means for learning existing material and for developing new results. One major insight of this type is the connection between the purely analytical character of an optimization problem, expressed perhaps by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. This was a major theme of the first and second editions. Now the third edition of Linear and Nonlinear Programming has been completely updated with recent Optimization Methods. Yinyu Ye has written chapters and chapter material on a number of these areas including Interior Point Methods.

Author Biography

David G. Luenberger has directed much of his career toward teaching "portable concepts" - organizing theory around concepts and actually "porting" the concepts to applications where, in the process, the general concepts are often discovered. The search for fundamentals has explicitly directed his research in the fields of control, optimization, planning, economics, and investments, and in turn, it is the discovery of these fundamentals that have motivated his textbook writing projects.

Table of Contents

Introductionp. 1
Optimizationp. 1
Types of Problemsp. 2
Size of Problemsp. 5
Iterative Algorithms and Convergencep. 6
Linear Programming
Basic Properties of Linear Programsp. 11
Introductionp. 11
Examples of Linear Programming Problemsp. 14
Basic Solutionsp. 19
The Fundamental Theorem of Linear Programmingp. 20
Relations to Convexityp. 22
Exercisesp. 28
The Simplex Methodp. 33
Pivotsp. 33
Adjacent Extreme Pointsp. 38
Determining a Minimum Feasible Solutionp. 42
Computational Procedure-Simplex Methodp. 46
Artificial Variablesp. 50
Matrix Form of the Simplex Methodp. 54
The Revised Simplex Methodp. 56
The Simplex Method and LU Decompositionp. 59
Decompositionp. 62
Summaryp. 70
Exercisesp. 70
Dualityp. 79
Dual Linear Programsp. 79
The Duality Theoremp. 82
Relations to the Simplex Procedurep. 84
Sensitivity and Complementary Slacknessp. 88
The Dual Simplex Methodp. 90
The Primal-Dual Algorithmp. 93
Reduction of Linear Inequalitiesp. 98
Exercisesp. 103
Interior-Point Methodsp. 111
Elements of Complexity Theoryp. 112
The Simplex Method is not Polynomial-Timep. 114
The Ellipsoid Methodp. 115
The Analytic Centerp. 118
The Central Pathp. 121
Solution Strategiesp. 126
Termination and Initializationp. 134
Summaryp. 139
Exercisesp. 140
Transportation and Network Flow Problemsp. 145
The Transportation Problemp. 145
Finding a Basic Feasible Solutionp. 148
Basis Triangularityp. 150
Simplex Method for Transportation Problemsp. 153
The Assignment Problemp. 159
Basic Network Conceptsp. 160
Minimum Cost Flowp. 162
Maximal Flowp. 166
Summaryp. 174
Exercisesp. 175
Unconstrained Problems
Basic Properties of Solutions and Algorithmsp. 183
First-Order Necessary Conditionsp. 184
Examples of Unconstrained Problemsp. 186
Second-Order Conditionsp. 190
Convex and Concave Functionsp. 192
Minimization and Maximization of Convex Functionsp. 197
Zero-Order Conditionsp. 198
Global Convergence of Descent Algorithmsp. 201
Speed of Convergencep. 208
Summaryp. 212
Exercisesp. 213
Basic Descent Methodsp. 215
Fibonacci and Golden Section Searchp. 216
Line Search by Curve Fittingp. 219
Global Convergence of Curve Fittingp. 226
Closedness of Line Search Algorithmsp. 228
Inaccurate Line Searchp. 230
The Method of Steepest Descentp. 233
Applications of the Theoryp. 242
Newton's Methodp. 246
Coordinate Descent Methodsp. 253
Spacer Stepsp. 255
Summaryp. 256
Exercisesp. 257
Conjugate Direction Methodsp. 263
Conjugate Directionsp. 263
Descent Properties of the Conjugate Direction Methodp. 266
The Conjugate Gradient Methodp. 268
The C-G Method as an Optimal Processp. 271
The Partial Conjugate Gradient Methodp. 273
Extension to Nonquadratic Problemsp. 277
Parallel Tangentsp. 279
Exercisesp. 282
Quasi-Newton Methodsp. 285
Modified Newton Methodp. 285
Construction of the Inversep. 288
Davidon-Fletcher-Powell Methodp. 290
The Broyden Familyp. 293
Convergence Propertiesp. 296
Scalingp. 299
Memoryless Quasi-Newton Methodsp. 304
Combination of Steepest Descent and Newton's Methodp. 306
Summaryp. 312
Exercisesp. 313
Constrained Minimization
Constrained Minimization Conditionsp. 321
Constraintsp. 321
Tangent Planep. 323
First-Order Necessary Conditions (Equality Constraints)p. 326
Examplesp. 327
Second-Order Conditionsp. 333
Eigenvalues in Tangent Subspacep. 335
Sensitivityp. 339
Inequality Constraintsp. 341
Zero-Order Conditions and Lagrange Multipliersp. 346
Summaryp. 353
Exercisesp. 354
Primal Methodsp. 359
Advantage of Primal Methodsp. 359
Feasible Direction Methodsp. 360
Active Set Methodsp. 363
The Gradient Projection Methodp. 367
Convergence Rate of the Gradient Projection Methodp. 374
The Reduced Gradient Methodp. 382
Convergence Rate of the Reduced Gradient Methodp. 387
Variationsp. 394
Summaryp. 396
Exercisesp. 396
Penalty and Barrier Methodsp. 401
Penalty Methodsp. 402
Barrier Methodsp. 405
Properties of Penalty and Barrier Functionsp. 407
Newton's Method and Penalty Functionsp. 416
Conjugate Gradients and Penalty Methodsp. 418
Normalization of Penalty Functionsp. 420
Penalty Functions and Gradient Projectionp. 421
Exact Penalty Functionsp. 425
Summaryp. 429
Exercisesp. 430
Dual and Cutting Plane Methodsp. 435
Global Dualityp. 435
Local Dualityp. 441
Dual Canonical Convergence Ratep. 446
Separable Problemsp. 447
Augmented Lagrangiansp. 451
The Dual Viewpointp. 456
Cutting Plane Methodsp. 460
Kelley's Convex Cutting Plane Algorithmp. 463
Modificationsp. 465
Exercisesp. 466
Primal-Dual Methodsp. 469
The Standard Problemp. 469
Strategiesp. 471
A Simple Merit Functionp. 472
Basic Primal-Dual Methodsp. 474
Modified Newton Methodsp. 479
Descent Propertiesp. 481
Rate of Convergencep. 485
Interior Point Methodsp. 487
Semidefinite Programmingp. 491
Summaryp. 498
Exercisesp. 499
Mathematical Reviewp. 507
Setsp. 507
Matrix Notationp. 508
Spacesp. 509
Eigenvalues and Quadratic Formsp. 510
Topological Conceptsp. 511
Functionsp. 512
Convex Setsp. 515
Basic Definitionsp. 515
Hyperplanes and Polytopesp. 517
Separating and Supporting Hyperplanesp. 519
Extreme Pointsp. 521
Gaussian Eliminationp. 523
Bibliographyp. 527
Indexp. 541
Table of Contents provided by Ingram. All Rights Reserved.

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