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, 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.
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Editorial Reviews
Book Description
A self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Exercises are included in each chapter, and additional information is provided in two appendices and an accompanying Web site. Only a basic knowledge of linear algebra and calculus is required.
About the Author
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.
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.
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1 of 1 people found the following review helpful:
2.0 out of 5 stars
Weak on theory and,
By Billy (Texas) - See all my reviews
This review is from: Linear Programming with MATLAB (MPS-SIAM Series on Optimization) (Paperback)
This book was used for a graduate course in LP. For that purpose it was very weak. It takes a practical view of LP and relates it to Matlab, just as the title suggests. I found its content more applicable to undergrads than grads.
On the positive side, the book includes a large set of Matlab routines for manipulating LP tableaus. The support routines are great for solving problems quickly. I would not recommend this for graduate work. I like Bazaraa's treatment of this subject much better. This book might be OK for professional work if you need a practical intro.
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Inside This Book
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First Sentence:
In Section 1.4 we sketched a geometric interpretation of the simplex method. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
unbounded tableau, feasible tableau, nonhasic variables, optimal tableau, unbounded ray, loss matrices, dual feasible point, revised simplex method, dual simplex method, solve the following linear program, pivot column, initial basic feasible solution, initial tableau, tableau representation, following quadratic program, negative reduced cost, pivot row, corresponding dual problem, optimal objective value, smallest subscript, resulting tableau, following tableau, block pivot, pivot rules, entering variable Key Phrases - Capitalized Phrases (CAPs): (learn more)
Path-Following Algorithm Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
In Section 1.4 we sketched a geometric interpretation of the simplex method. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
unbounded tableau, feasible tableau, nonhasic variables, optimal tableau, unbounded ray, loss matrices, dual feasible point, revised simplex method, dual simplex method, solve the following linear program, pivot column, initial basic feasible solution, initial tableau, tableau representation, following quadratic program, negative reduced cost, pivot row, corresponding dual problem, optimal objective value, smallest subscript, resulting tableau, following tableau, block pivot, pivot rules, entering variable Key Phrases - Capitalized Phrases (CAPs): (learn more)
Path-Following Algorithm Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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