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Introduction to Discrete Optimization. Computational Complexity. Polynomial Algorithms-Matroids Enumeration Algorithms. Polynomial Algorithms. Linear Programming Nonpolynomial algorithims-Partial Enumeration Nonpolynomial Algorithims-Polyhedral Description Nonexact Algorithms Vector Matrices and Convex Sets Graph Theory Fundamentals Linear Programming Fundamentals.
This book treats the fundamental issues and algorithmic strategies emerging as the core of the discipline of discrete optimization in a comprehensive and rigorous fashion. Following an introductory chapter on computational complexity, the basic algorithmic results for the two major models of polynomial algorithms are introduced--models using matroids and linear programming. Further chapters treat the major non-polynomial algorithms: branch-and-bound and cutting planes. The text concludes with a chapter on heuristic algorithms. Several appendixes are included which review the fundamental ideas of linear programming, graph theory, and combinatorics--prerequisites for readers of the text. Numerous exercises are included at the end of each chapter.
- No. of pages:
- © Academic Press 1988
- 28th August 1988
- Academic Press
- eBook ISBN:
@qu:Accessible to students, researchers, and instructors, this work would be valuable both for a university course in discrete optimization and as a reference book. The references are good and up to date. @source:--COMPUTING REVIEWS @qu:The book is cleary and beautifully written. It is a very useful reference on the subject and can be used as an advanced graduate text for courses in combinatorial or discrete optimization. @source:--MATHEMATICAL REVIEWS @qu:I can unreservedly recommend this book to any lecturer preparing a course building on an introductory course on basic linear and network programming. It finds its deserved place next to my favorite textbooks on linear, integer, and combinatorial optimization. @source:--Uwe Zimmermann @qu:In a single volume, they succeed in covering most of the important basic and advanced material. I can unreservedly recommend this book to any lecturer preparing a course building on an introductory course on basic linear and network programming. The book's strength lies in its succinct introductions to the valuable ideas that are treated more verbosely in the specialized books. It finds its deserved place next to my favorite textbooks on linear, integer, and combinatorial optimization. @source:--SIAM REVIEW
Georgia Institute of Technology
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