Ebook: Linear Programming and Generalizations: A Problem-based Introduction with Spreadsheets

This book on constrained optimization is novel in that it fuses these themes: • use examples to introduce general ideas; • engage the student in spreadsheet computation; • survey the uses of constrained optimization;. • investigate game theory and nonlinear optimization, • link the subject to economic reasoning, and • present the requisite mathematics. Blending these themes makes constrained optimization more accessible and more valuable. It stimulates the student’s interest, quickens the learning process, reveals connections to several academic and professional fields, and deepens the student’s grasp of the relevant mathematics. The book is designed for use in courses that focus on the applications of constrained optimization, in courses that emphasize the theory, and in courses that link the subject to economics.

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The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems. Although readily accessible to readers with a modest background in computational mathematics, it is also intended to be of interest to researchers in the field. Introduction to Derivative-Free Optimization is the first contemporary comprehensive treatment of optimization without derivatives. This book covers most of the relevant classes of algorithms from direct search to model-based approaches. It contains a comprehensive description of the sampling and modeling tools needed for derivative-free optimization; these tools allow the reader to better understand the convergent properties of the algorithms and identify their differences and similarities. Introduction to Derivative-Free Optimization also contains analysis of convergence for modified Nelder Mead and implicit-filtering methods, as well as for model-based methods such as wedge methods and methods based on minimum-norm Frobenius models. Audience: The book is intended for anyone interested in using optimization on problems where derivatives are difficult or impossible to obtain. Such audiences include chemical, mechanical, aeronautical, and electrical engineers, as well as economists, statisticians, operations researchers, management scientists, biological and medical researchers, and computer scientists. It is also appropriate for use in an advanced undergraduate or early graduate-level course on optimization for students having a background in calculus, linear algebra, and numerical analysis. Contents: Preface; Chapter 1: Introduction; Part I: Sampling and modeling; Chapter 2: Sampling and linear models; Chapter 3: Interpolating nonlinear models; Chapter 4: Regression nonlinear models; Chapter 5: Underdetermined interpolating models; Chapter 6: Ensuring well poisedness and suitable derivative-free models; Part II: Frameworks and algorithms; Chapter 7: Directional direct-search methods; Chapter 8: Simplicial direct-search methods; Chapter 9: Line-search methods based on simplex derivatives; Chapter 10: Trust-region methods based on derivative-free models; Chapter 11: Trust-region interpolation-based methods; Part III: Review of other topics; Chapter 12: Review of surrogate model management; Chapter 13: Review of constrained and other extensions to derivative-free optimization; Appendix: Software for derivative-free optimization; Bibliography; Index Chapter 1. Introduction to Linear Programs.- Chapter 2. Spreadsheet Computation.- Chapter 3. Mathematical Preliminaries.- Chapter 4. The Simplex Method, Part 1.- Chapter 5. Analyzing Linear Programs.- Chapter 6. The Simplex Method, Part 2.- Chapter 7. A Survey of Optimization Problems.- Chapter 8. Path-Length Problems and Dynamic Programming.- Chapter 9. Flows in Networks.- Chapter 10. Vector Spaces and Linear Programs.- Chapter 11. Multipliers and the Simplex Method.- Chapter 12. Duality.- Chapter 13. The Dual Simplex Pivot and Its Uses.- Chapter 14. Introduction to Game Theory.- Chapter 15. The Bi-Matrix Game.- Chapter 16. Fixed Points and Equilibria.- Chapter 17. Convex Sets.- Chapter 18. Differentiation.- Chapter 19. Convex Functions.- Chapter 20.- Nonlinear Programs