Antonio .J. Conejo, Enrique Castillo, Roberto Minguez, Raquel Garcia-Bertrand, ?Decomposition Techniques in Mathematical Progra

   
This textbook for students and practitioners presents a practical approach to decomposition techniques in optimization. It provides an appropriate blend of theoretical background and practical applications in engineering and science, which makes the book interesting for practitioners, as well as engineering, operations research and applied economics graduate and postgraduate students. "Decomposition Techniques in Mathematical Programming" is based on clarifying, illustrative and computational examples and applications from electrical, mechanical, energy and civil engineering as well as applied mathematics and economics. It addresses decomposition in linear programming, mixed-integer linear programming, nonlinear programming, and mixed-integer nonlinear programming, and provides rigorous decomposition algorithms as well as heuristic ones. Practical applications are developed up to working algorithms that can be readily used. The theoretical background of the book is deep enough to be of interest to applied mathematicians. It includes end of chapter exercises and the solutions of the even numbered exercises are included as an appendix.

Table of Contents

1 Motivating examples 3
2 Linear programming : complicating constraints 67
3 Linear programming : complicating variables 107
4 Duality 141
5 Decomposition in nonlinear programming 187
6 Decomposition in mixed-integer programming 243
7 Other decomposition techniques 271
8 Local sensitivity analysis 303
9 Applications 349
A Some GAMS implementations 397
B Exercise solutions 421



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