2007 / xii + 266 pages / Softcover / ISBN: 978-0-898716-43-6 / List Price $51.00 / MOS/SIAM Member Price $35.70 / Order Code MP07
Keywords: linear programming, optimization, MATLAB routines
"…an excellent choice for classroom use as well as a resource for mathematically prepared researchers unfamiliar with the subject." —Richard Cottle,Optimization Methods and Software, 23(5)
"Written by leading scientists in the area, this textbook provides a beautiful introduction to standard and novel methods and algorithms for linear and quadratic programming." —Renato De Leone, Computing Reviews, July 2008
This textbook provides a self-contained introduction to linear programming using MATLAB® 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, and 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.
About the Authors
Michael C. Ferris is a Professor in the Computer Sciences Department at the University of Wisconsin-Madison. His research focuses on algorithmic and interface development for large-scale problems in mathematical programming. He serves as associate editor of the SIAM Journal on Optimizationand coeditor of Mathematical Programming.
Olvi L. Mangasarian is John von Neumann Professor Emeritus of Mathematics and Computer Sciences at the University of Wisconsin-Madison. His current research centers on mathematical programming applications to machine learning and data mining. He is also a Research Scientist at the University of California at San Diego and author of Nonlinear Programming (SIAM, 1994).
Stephen J. Wright is a Professor in the Computer Sciences Department at the University of Wisconsin-Madison. His research interests lie in computational optimization and its applications to all areas of science and engineering. He is author of Primal-Dual Interior-Point Methods (SIAM, 1997) and coauthor of Numerical Optimization (Springer, 2006).
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