Combinatorial optimization algorithms and complexity pdf

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• Combinatorial Optimization: Algorithms and Complexity
• Computing in Combinatorial Optimization
• Donate to arXiv

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Combinatorial Optimization: Algorithms and Complexity

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Papadimitriou and K. Papadimitriou , K. Steiglitz Published Mathematics, Computer Science. This clearly written , mathematically rigorous text includes a novel algorithmic exposition of the simplex method and also discusses the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; approximation algorithms, local search heuristics for NPcomplete problems, more.

This is a Dover reprint of a classic textbook originally published in The book is about combinatorial optimization problems, their computational complexity, and algorithms for their solution. It begins with eight chapters on the simplex method for linear programming and network flow problems. The ellipsoid algorithm is introduced as a polynomial time algorithm for linear programming in chapter 8. The remaining chapters of the book discuss polynomial time algorithms for various combinatorial optimization problems, NP-Completeness, and approaches to dealing with NP-Complete problems including integer linear programming, meta heuristics, and approximation algorithms. At the time of its original publication, this book provided a broad overview of the entire field of combinatorial optimization and introduced many significant new areas of research. Although the book is still very readable as an introduction to combinatorial optimization and NP-Completeness, it can no longer be recommended as an up-to-date source on these subjects.

Computing in Combinatorial Optimization

Computing and Software Science pp Cite as. Research in combinatorial optimization successfully combines diverse ideas drawn from computer science, mathematics, and operations research. We give a tour of this work, focusing on the early development of the subject and the central role played by linear programming. The paper concludes with a short wish list of future research directions. The design of efficient algorithms for combinatorial problems has long been a target of computer science research.

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This updated and revised 2nd edition of the three-volume Combinatorial Optimization series covers a very large set of topics in this area, dealing with fundamental notions and approaches as well as several classical applications of Combinatorial Optimization. Combinatorial Optimization is a multidisciplinary field, lying at the interface of three major scientific domains: applied mathematics, theoretical computer science, and management studies. Its focus is on finding the least-cost solution to a mathematical problem in which each solution is associated with a numerical cost.