Linear Programming: Foundations and Extensions
This book is an introductory graduate textbook on linear programming
although upper-level graduate students and researchers will find plenty
material here that cannot be found in other books. It has also been used
successfully to teach undergraduates majoring in Operations Research.
Balanced treatment of the simplex method and interior-point methods.
Efficient source code (in C) for all the algorithms presented in the
Thorough discussion of several interior-point methods including
path-following, affine-scaling, and homogeneous self dual methods.
Extensive coverage of applications including traditional topics such
network flows and game theory as well as less familiar ones such as
structural optimization, L^1 regression, and the Markowitz
portfolio optimization model.
Over 200 class-tested exercises.
A dynamically expanding collection of exercises.
Some Early Adoptions
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- City University of New York
- Clemson University
- Cornell University
- Munich University of Applied Sciences
- Georgia Institute of Technology
- Hunter College
- Johns Hopkins University
- NYU--Courant Institute of Mathematical Sciences
- Rutgers University
- Pennsylvania State University
- Pontificia Universidade Catlica do Rio de Janeiro
- Princeton University
- Tokyo Institute of Technology
- Universidad Simon Bolivar
- Universidade Federal do Rio De Janeiro
- University of Arizona
- University of California at Davis
- University of California at Los Angeles
- University of Connecticut
- University of Florida at Gainesville
- University of Maryland
- University of Oklahoma
- University of Pennsylvania
- University of Pittsburgh
- University of South Florida
- University of Tennessee at Knoxville
- University of Texas
- Utrecht University
- Yale University