
Linear and Nonlinear Programming
by Luenberger, David G.; Ye, Yinyu-
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Summary
Author Biography
Table of Contents
Introduction | p. 1 |
Optimization | p. 1 |
Types of Problems | p. 2 |
Size of Problems | p. 5 |
Iterative Algorithms and Convergence | p. 6 |
Linear Programming | |
Basic Properties of Linear Programs | p. 11 |
Introduction | p. 11 |
Examples of Linear Programming Problems | p. 14 |
Basic Solutions | p. 19 |
The Fundamental Theorem of Linear Programming | p. 20 |
Relations to Convexity | p. 22 |
Exercises | p. 28 |
The Simplex Method | p. 33 |
Pivots | p. 33 |
Adjacent Extreme Points | p. 38 |
Determining a Minimum Feasible Solution | p. 42 |
Computational Procedure-Simplex Method | p. 46 |
Artificial Variables | p. 50 |
Matrix Form of the Simplex Method | p. 54 |
The Revised Simplex Method | p. 56 |
The Simplex Method and LU Decomposition | p. 59 |
Decomposition | p. 62 |
Summary | p. 70 |
Exercises | p. 70 |
Duality | p. 79 |
Dual Linear Programs | p. 79 |
The Duality Theorem | p. 82 |
Relations to the Simplex Procedure | p. 84 |
Sensitivity and Complementary Slackness | p. 88 |
The Dual Simplex Method | p. 90 |
The Primal-Dual Algorithm | p. 93 |
Reduction of Linear Inequalities | p. 98 |
Exercises | p. 103 |
Interior-Point Methods | p. 111 |
Elements of Complexity Theory | p. 112 |
The Simplex Method is not Polynomial-Time | p. 114 |
The Ellipsoid Method | p. 115 |
The Analytic Center | p. 118 |
The Central Path | p. 121 |
Solution Strategies | p. 126 |
Termination and Initialization | p. 134 |
Summary | p. 139 |
Exercises | p. 140 |
Transportation and Network Flow Problems | p. 145 |
The Transportation Problem | p. 145 |
Finding a Basic Feasible Solution | p. 148 |
Basis Triangularity | p. 150 |
Simplex Method for Transportation Problems | p. 153 |
The Assignment Problem | p. 159 |
Basic Network Concepts | p. 160 |
Minimum Cost Flow | p. 162 |
Maximal Flow | p. 166 |
Summary | p. 174 |
Exercises | p. 175 |
Unconstrained Problems | |
Basic Properties of Solutions and Algorithms | p. 183 |
First-Order Necessary Conditions | p. 184 |
Examples of Unconstrained Problems | p. 186 |
Second-Order Conditions | p. 190 |
Convex and Concave Functions | p. 192 |
Minimization and Maximization of Convex Functions | p. 197 |
Zero-Order Conditions | p. 198 |
Global Convergence of Descent Algorithms | p. 201 |
Speed of Convergence | p. 208 |
Summary | p. 212 |
Exercises | p. 213 |
Basic Descent Methods | p. 215 |
Fibonacci and Golden Section Search | p. 216 |
Line Search by Curve Fitting | p. 219 |
Global Convergence of Curve Fitting | p. 226 |
Closedness of Line Search Algorithms | p. 228 |
Inaccurate Line Search | p. 230 |
The Method of Steepest Descent | p. 233 |
Applications of the Theory | p. 242 |
Newton's Method | p. 246 |
Coordinate Descent Methods | p. 253 |
Spacer Steps | p. 255 |
Summary | p. 256 |
Exercises | p. 257 |
Conjugate Direction Methods | p. 263 |
Conjugate Directions | p. 263 |
Descent Properties of the Conjugate Direction Method | p. 266 |
The Conjugate Gradient Method | p. 268 |
The C-G Method as an Optimal Process | p. 271 |
The Partial Conjugate Gradient Method | p. 273 |
Extension to Nonquadratic Problems | p. 277 |
Parallel Tangents | p. 279 |
Exercises | p. 282 |
Quasi-Newton Methods | p. 285 |
Modified Newton Method | p. 285 |
Construction of the Inverse | p. 288 |
Davidon-Fletcher-Powell Method | p. 290 |
The Broyden Family | p. 293 |
Convergence Properties | p. 296 |
Scaling | p. 299 |
Memoryless Quasi-Newton Methods | p. 304 |
Combination of Steepest Descent and Newton's Method | p. 306 |
Summary | p. 312 |
Exercises | p. 313 |
Constrained Minimization | |
Constrained Minimization Conditions | p. 321 |
Constraints | p. 321 |
Tangent Plane | p. 323 |
First-Order Necessary Conditions (Equality Constraints) | p. 326 |
Examples | p. 327 |
Second-Order Conditions | p. 333 |
Eigenvalues in Tangent Subspace | p. 335 |
Sensitivity | p. 339 |
Inequality Constraints | p. 341 |
Zero-Order Conditions and Lagrange Multipliers | p. 346 |
Summary | p. 353 |
Exercises | p. 354 |
Primal Methods | p. 359 |
Advantage of Primal Methods | p. 359 |
Feasible Direction Methods | p. 360 |
Active Set Methods | p. 363 |
The Gradient Projection Method | p. 367 |
Convergence Rate of the Gradient Projection Method | p. 374 |
The Reduced Gradient Method | p. 382 |
Convergence Rate of the Reduced Gradient Method | p. 387 |
Variations | p. 394 |
Summary | p. 396 |
Exercises | p. 396 |
Penalty and Barrier Methods | p. 401 |
Penalty Methods | p. 402 |
Barrier Methods | p. 405 |
Properties of Penalty and Barrier Functions | p. 407 |
Newton's Method and Penalty Functions | p. 416 |
Conjugate Gradients and Penalty Methods | p. 418 |
Normalization of Penalty Functions | p. 420 |
Penalty Functions and Gradient Projection | p. 421 |
Exact Penalty Functions | p. 425 |
Summary | p. 429 |
Exercises | p. 430 |
Dual and Cutting Plane Methods | p. 435 |
Global Duality | p. 435 |
Local Duality | p. 441 |
Dual Canonical Convergence Rate | p. 446 |
Separable Problems | p. 447 |
Augmented Lagrangians | p. 451 |
The Dual Viewpoint | p. 456 |
Cutting Plane Methods | p. 460 |
Kelley's Convex Cutting Plane Algorithm | p. 463 |
Modifications | p. 465 |
Exercises | p. 466 |
Primal-Dual Methods | p. 469 |
The Standard Problem | p. 469 |
Strategies | p. 471 |
A Simple Merit Function | p. 472 |
Basic Primal-Dual Methods | p. 474 |
Modified Newton Methods | p. 479 |
Descent Properties | p. 481 |
Rate of Convergence | p. 485 |
Interior Point Methods | p. 487 |
Semidefinite Programming | p. 491 |
Summary | p. 498 |
Exercises | p. 499 |
Mathematical Review | p. 507 |
Sets | p. 507 |
Matrix Notation | p. 508 |
Spaces | p. 509 |
Eigenvalues and Quadratic Forms | p. 510 |
Topological Concepts | p. 511 |
Functions | p. 512 |
Convex Sets | p. 515 |
Basic Definitions | p. 515 |
Hyperplanes and Polytopes | p. 517 |
Separating and Supporting Hyperplanes | p. 519 |
Extreme Points | p. 521 |
Gaussian Elimination | p. 523 |
Bibliography | p. 527 |
Index | p. 541 |
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