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Linear and Nonlinear Programming with Maple
An Interactive, Applications-Based Approach
By:Â Paul E. Fishback
Hardcover | 9 December 2009 | Edition Number 1
At a Glance
413 Pages
23.7 x 16.1 x 2.7
Hardcover
RRP $378.00
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List of Figures | p. xiii |
List of Tables | p. xv |
Foreword | p. xix |
Linear Programming | p. 1 |
An Introduction to Linear Programming | p. 3 |
The Basic Linear Programming Problem Formulation | p. 3 |
A Prototype Example: The Blending Problem | p. 4 |
Maple's LPSolve Command | p. 7 |
The Matrix Inequality Form of an LP | p. 8 |
Exercises | p. 10 |
Linear Programming: A Graphical Perspective in R2 | p. 13 |
Exercises | p. 17 |
Basic Feasible Solutions | p. 19 |
Exercises | p. 25 |
The Simplex Algorithm | p. 29 |
The Simplex Algorithm | p. 29 |
An Overview of the Algorithm | p. 29 |
A Step-by-Step Analysis of the Process | p. 30 |
Solving Minimization Problems | p. 33 |
A Step-by-Step Maple Implementation of the Simplex Algorithm | p. 34 |
Exercises | p. 38 |
Alternative Optimal/Unbounded Solutions and Degeneracy | p. 39 |
Alternative Optimal Solutions | p. 40 |
Unbounded Solutions | p. 41 |
Degeneracy | p. 42 |
Exercises | p. 45 |
Excess and Artificial Variables: The Big M Method | p. 47 |
Exercises | p. 53 |
A Partitioned Matrix View of the Simplex Method | p. 54 |
Partitioned Matrices | p. 54 |
Partitioned Matrices with Maple | p. 55 |
The Simplex Algorithm as Partitioned Matrix Multiplication | p. 56 |
Exercises | p. 61 |
The Revised Simplex Algorithm | p. 62 |
Notation | p. 62 |
Observations about the Simplex Algorithm | p. 63 |
An Outline of the Method | p. 63 |
Application to the FuelPro LP | p. 64 |
Exercises | p. 67 |
Moving beyond the Simplex Method: An Interior Point Algorithm | p. 68 |
The Origin of the Interior Point Algorithm | p. 68 |
The Projected Gradient | p. 69 |
Affine Scaling | p. 72 |
Summary of the Method | p. 75 |
Application of the Method to the FuelPro LP | p. 75 |
A Maple Implementation of the Interior Point Algorithm | p. 76 |
Exercises | p. 79 |
Standard Applications of Linear Programming | p. 81 |
The Diet Problem | p. 81 |
Eating for Cheap on a Very Limited Menu | p. 81 |
The Problem Formulation and Solution, with Help from Maple | p. 82 |
Exercises | p. 85 |
Transportation and Transshipment Problems | p. 85 |
A Coal Distribution Problem | p. 85 |
The Integrality of the Transportation Problem Solution | p. 87 |
Coal Distribution with Transshipment | p. 89 |
Exercises | p. 91 |
Basic Network Models | p. 92 |
The Minimum Cost Network Flow Problem Formulation | p. 92 |
Formulating and Solving the Minimum Cost Network Flow Problem with Maple | p. 94 |
The Shortest Path Problem | p. 95 |
Maximum Flow Problems | p. 98 |
Exercises | p. 99 |
Duality and Sensitivity Analysis | p. 103 |
Duality | p. 103 |
The Dual of an LP | p. 103 |
Weak and Strong Duality | p. 105 |
An Economic Interpretation of Duality | p. 110 |
A Final Note on the Dual of an Arbitrary LP | p. 111 |
The Zero-Sum Matrix Game | p. 112 |
Exercises | p. 116 |
Sensitivity Analysis | p. 119 |
Sensitivity to an Objective Coefficient | p. 121 |
Sensitivity to Constraint Bounds | p. 125 |
Sensitivity to Entries in the Coefficient Matrix A | p. 130 |
Performing Sensitivity Analysis with Maple | p. 133 |
Exercises | p. 135 |
The Dual Simplex Method | p. 137 |
Overview of the Method | p. 138 |
A Simple Example | p. 139 |
Exercises | p. 143 |
Integer Linear Programming | p. 145 |
An Introduction to Integer Linear Programming and the Branch and Bound Method | p. 145 |
A Simple Example | p. 145 |
The Relaxation of an ILP | p. 146 |
The Branch and Bound Method | p. 147 |
Practicing the Branch and Bound Method with Maple | p. 154 |
Binary and Mixed Integer Linear Programming | p. 155 |
Solving ILPs Directly with Maple | p. 156 |
An Application of Integer Linear Programming: The Traveling Sales person Problem | p. 157 |
Exercises | p. 162 |
The Cutting Plane Algorithm | p. 167 |
Motivation | p. 167 |
The Algorithm | p. 168 |
A Step-by-Step Maple Implementation of the Cutting Plane Algorithm | p. 172 |
Comparison with the Branch and Bound Method | p. 175 |
Exercises | p. 175 |
Nonlinear Programming | p. 177 |
Algebraic Methods for Unconstrained Problems | p. 179 |
Nonlinear Programming: An Overview | p. 179 |
The General Nonlinear Programming Model | p. 179 |
Plotting Feasible Regions and Solving NLPs with Maple | p. 180 |
A Prototype NLP Example | p. 183 |
Exercises | p. 185 |
Differentiability and a Necessary First-Order Condition | p. 187 |
Differentiability | p. 188 |
Necessary Conditions for Local Maxima or Minima | p. 190 |
Exercises | p. 193 |
Convexity and a Sufficient First-Order Condition | p. 193 |
Convexity | p. 194 |
Testing for Convexity | p. 196 |
Convexity and The Global Optimal Solutions Theorem | p. 199 |
Solving the Unconstrained NLP for Differentiable, Convex Functions | p. 200 |
Multiple Linear Regression | p. 201 |
Exercises | p. 204 |
Sufficient Conditions for Local and Global Optimal Solutions | p. 206 |
Quadratic Forms | p. 207 |
Positive Definite Quadratic Forms | p. 209 |
Second-Order Differentiability and the Hessian Matrix | p. 210 |
Using Maple to Classify Critical Points for the Unconstrained NLP | p. 218 |
The Zero-Sum Matrix Game, Revisited | p. 219 |
Exercises | p. 222 |
Numeric Tools for Unconstrained NLPs | p. 225 |
The Steepest Descent Method | p. 225 |
Method Derivation | p. 225 |
A Maple Implementation of the Steepest Descent Method | p. 229 |
A Sufficient Condition for Convergence | p. 231 |
The Rate of Convergence | p. 234 |
Exercises | p. 236 |
Newton's Method | p. 238 |
Shortcomings of the Steepest Descent Method | p. 238 |
Method Derivation | p. 239 |
A Maple Implementation of Newton's Method | p. 241 |
Convergence Issues and Comparison with the Steepest Descent Method | p. 243 |
Exercises | p. 247 |
The Levenberg-Marquardt Algorithm | p. 249 |
Interpolating between the Steepest Descent and Newton Methods | p. 249 |
The Levenberg Method | p. 250 |
The Levenberg-Marquardt Algorithm | p. 251 |
A Maple Implementation of the Levenberg-Marquardt Algorithm | p. 253 |
Nonlinear Regression | p. 255 |
Maple's Global Optimization Toolbox | p. 257 |
Exercises | p. 258 |
Methods for Constrained Nonlinear Problems | p. 261 |
The Lagrangian Function and Lagrange Multipliers | p. 261 |
Some Convenient Notation | p. 262 |
The Karush-Kuhn-Tucker Theorem | p. 263 |
Interpreting the Multiplier | p. 267 |
Exercises | p. 269 |
Convex NLPs | p. 272 |
Solving Convex NLPs | p. 273 |
Exercises | p. 276 |
Saddle Point Criteria | p. 278 |
The Restricted Lagrangian | p. 278 |
Saddle Point Optimality Criteria | p. 280 |
Exercises | p. 282 |
Quadratic Programming | p. 284 |
Problems with Equality-type Constraints Only | p. 284 |
Inequality Constraints | p. 289 |
Maple's QPSolve Command | p. 291 |
The Bimatrix Game | p. 293 |
Exercises | p. 297 |
Sequential Quadratic Programming | p. 300 |
Method Derivation for Equality-type Constraints | p. 300 |
The Convergence Issue | p. 306 |
Inequality-Type Constraints | p. 306 |
A Maple Implementation of the Sequential Quadratic Programming Technique | p. 310 |
An Improved Version of the SQPT | p. 312 |
Exercises | p. 315 |
Projects | p. 319 |
Excavating and Leveling a Large Land Tract | p. 319 |
The Juice Logistics Model | p. 322 |
Work Scheduling with Overtime | p. 325 |
Diagnosing Breast Cancer with a Linear Classifier | p. 327 |
The Markowitz Portfolio Model | p. 330 |
A Game Theory Model of a Predator-Prey Habitat | p. 334 |
Important Results from Linear Algebra | p. 337 |
Linear Independence | p. 337 |
The Invertible Matrix Theorem | p. 337 |
Transpose Properties | p. 338 |
Positive Definite Matrices | p. 338 |
Cramer's Rule | p. 339 |
The Rank-Nullity Theorem | p. 339 |
The Spectral Theorem | p. 339 |
Matrix Norms | p. 340 |
Getting Started with Maple | p. 341 |
The Worksheet Structure | p. 341 |
Arithmetic Calculations and Built-in Operations | p. 343 |
Expressions and Functions | p. 344 |
Arrays, Lists, Sequences, and Sums | p. 347 |
Matrix Algebra and the LinearAlgebra Package | p. 349 |
Plot Structures with Maple | p. 353 |
Summary of Maple Commands | p. 363 |
Bibliography | p. 383 |
Index | p. 387 |
Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9781420090642
ISBN-10: 142009064X
Series: Textbooks in Mathematics
Published: 9th December 2009
Format: Hardcover
Language: English
Number of Pages: 413
Audience: Professional and Scholarly
Publisher: Taylor & Francis Ltd
Country of Publication: GB
Edition Number: 1
Dimensions (cm): 23.7 x 16.1 x 2.7
Weight (kg): 0.7
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