This textbook provides a self-contained introduction to linear programming using MATLAB(R) software to elucidate the development of algorithms and theory. Early chapters cover linear algebra basics, the simplex method, duality, the solving of large linear problems, sensitivity analysis, and parametric linear programming. In later chapters, the authors discuss quadratic programming, linear complementarity, interior-point methods, and selected applications of linear programming to approximation and classification problems. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, nonlinear functions, and on available MATLAB commands, respectively. Readers can access MATLAB codes and associated mex files at a Web site maintained by the authors. Only a basic knowledge of linear algebra and calculus is required to understand this textbook, which is geared toward junior and senior-level undergraduate students, first-year graduate students, and researchers unfamiliar with linear programming.
Michael C. Ferris is a Professor in the Computer Sciences Department at the University of Wisconsin-Madison. Olvi L. Mangasarian is John von Neumann Professor Emeritus of Mathematics and Computer Sciences at the University of Wisconsin-Madison. Stephen J. Wright is a Professor in the Computer Sciences Department at the University of Wisconsin-Madison.
1. Introduction; 2. Linear algebra; 3. The simplex method; 4. Duality; 5. Solving large linear programs; 6. Sensitivity and parametric linear programming; 7. Quadratic programming and complementarity problems; 8. Interior point methods; 9. Approximation and classification; A. Linear algebra, convexity, and nonlinear functions; B. Summary of available MATLAB Commands; Bibliography; Index.
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- ID: 9780898716436
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