Combinatorial Optimization: Algorithms and Complexity (Dover Books on Computer Science) Unabridged Edition

This is a very nice, self-contained introduction to linear programming, algorithm design and analysis, and computational complexity. The contents are as follows:Chap. 1 Optimization Problems 1.1 Introduction; 1.2 Optimization Problems; 1.3 Neighborhoods; 1.4 Local and Global Optima; 1.5 Convex Sets and Functions; 1.6 Convex Programming ProblemsChap. 2 The Simplex Algorithm 2.1 Forms of the Linear Programming Problem; 2.2 Basic Feasible Solutions; 2.3 The Geometry of Linear Programs; 2.3.1 Linear and Affine Spaces; 2.3.2 Convex Polytopes; 2.3.3 Polytopes and LP; 2.4 Moving from bfs to bfs; 2.5 Organization of a Tableau; 2.6 Choosing a Profitable Column; 2.7 Degeneracy and Bland's Anticycling Algorithm; 2.8 Beginning the Simplex Algorithm; 2.9 Geometric Aspects of PivotingChap. 3 Duality 3.1 The Dual of a Linear Program in General Form; 3.2 Complementary Slackness; 3.3 Farkas' Lemma; 3.4 The Shortest-Path Problem and Its Dual; 3.5 Dual Information in the Tableau; 3.6 The Dual Simplex Algorithm; 3.7 Interpretation of the Dual Simplex AlgorithmChap. 4 Computational Considerations for the Simplex Algorithm 4.1 The Revised Simplex Algorithm; 4.2 Compuational Implications of the Revised Simplex Algorithm; 4.3 The Max-Flow Problem and Its Solution by the Revised Method; 4.4 Dantzig-Wolfe DecompositionChap. 5 The Primal-Dual Algorithm 5.1 Introduction; 5.2 The Primal-Dual Algorithm; 5.3 Comments on the Primal-Dual Algorithm; 5.4 The Primal-Dual Method Applied to the Shortest-Path Problem; 5.5 Comments on Methodology; 5.6 The Primal-Dual Method Applied to Max-FlowChap. 6 Primal-Dual Algorithms for Max-Flow and Shortest Path: Ford-Fulkerson and Dijkstra 6.1 The Max-Flow, Min-Cut Theorem; 6.2 The Ford and Fulkerson Labeling Algorithm; 6.3 The Question of Finiteness of the Labeling Algorithm; 6.4 Dijkstra's Algorithm; 6.5 The Floyd-Warshall AlgorithmChap. 7 Primal-Dual Algorithms for Min-Cost Flow 7.1 The Min-Cost Flow Problem; 7.2 Combinatorializing the Capacities--Algorithm Cycle; 7.3 Combinatorializing the Cost--Algorithm Buildup; 7.4 An Explicit Primal-Dual Algorithm for the Hitchcock Problem--Algorithm Alphabeta; 7.5 A Transformation of Min-Cost Flow to Hitchcock; 7.6 ConclusionChap. 8 Algorithms and Complexity 8.1 Computability; 8.2 Time Bounds; 8.3 The Size of an Instance; 8.4 Analysis of Algorithms; 8.5 Polynomial-Time Algorithms; 8.6 Simplex Is Not a Polynomial-Time Algorithm; 8.7 The Ellipsoid Algorithm; 8.7.1 LP, LI, and LSI; 8.7.2 Affine Transformations and Ellipsoids; 8.7.3 The Algorithm; 8.7.4 Arithmetic PrecisionChap. 9 Efficient Algorithms for the Max-Flow Problem 9.1 Graph Search; 9.2 What Is Wrong With the Labeling Algorithm; 9.3 Network Labeling and Digraph Search; 9.4 An O(|V|²) Max-Flow Algorithm; 9.5 The Case of Unit CapacitiesChap. 10 Algorithms For Matching 10.1 The Matching Problem; 10.2 A Bipartite Matching Algorithm; 10.3 Bipartite Matching and Network Flow; 10.4 Nonbipartite Matching: Blossoms; 10.5 Nonbipartite Matching: An AlgorithmChap. 11 Weighted Matching 11.1 Introduction; 11.2 The Hungarian Method for the Assignment Problem; 11.3 The Nonbipartite Weighted Matching Problem; 11.4 ConclusionsChap. 12 Spanning Trees and Matroids 12.1 The Minimum Spanning Tree Problem; 12.2 An O(|E|log|V|) Algorithm for the Minimum Spanning Tree Problem; 12.3 The Greedy Algorithm; 12.4 Matroids; 12.5 The Intersection of Two Matroids; 12.6 On Certain Extensions of the Matroid Intersection Problem; 12.6.1 Weighted Matroid Intersection; 12.6.2 Matroid Parity; 12.6.3 The Intersection of Three MatroidsChap. 13 Interger Linear Programming 13.1 Introduction; 13.2 Total Unimodularity; 13.3 Upper Bounds for Solutions of ILPsChap. 14 A Cutting-Plane Algorithm for Integer Linear Programs 14.1 Gomory Cuts; 14.2 Lexicography; 14.3 Finiteness of the Fractional Dual Algorithm; 14.4 Other Cutting-Plane AlgorithmsChap. 15 NP-Complete Problems 15.1 Introduction; 15.2 An Optimization Problem Is Three Problems; 15.3 The Classes P and NP; 15.4 Polynomial-Time Reductions; 15.5 Cook's Theorem; 15.6 Some Other NP-Complete Problems: Clique and the TSP; 15.7 More NP-Complete Problems: Matching, Covering, and PartitioningChap. 16 More About NP-Completeness 16.1 The Class co-NP; 16.2 Pseudo-Polynomial Algorithms and "Strong" NP-Complete Problems; 16.3 Special Cases and Generalizations of NP-Complete Problems; 16.3.1 NP-Completeness By Restriction; 16.3.2 Easy Special Cases of NP-Complete Problems; 16.3.3 Hard Special Cases of NP-Complete Problems; 16.4 A Glossary of Related Concepts; 16.4.1 Polynomial-Time Reductions; 16.4.2 NP-Hard problems; 16.4.3 Nondeterministic Turing Machines; 16.4.4 Polynomial-Space Complete Problems; 16.5 EpilogueChap. 17 Approximation Algorithms 17.1 Heuristics for Node Cover: An Example; 17.2 Approximation Algorithm for the Traveling Salesman Problem; 17.3 Approximation Schemes; 17.4 Negative ResultsChap. 18 Branch-and-Bound and Dynamic Programming 18.1 Branch-and-Bound for Integer Linear Programming; 18.2 Branch-and-Bound in a General Context; 18.3 Dominance Relations; 18.4 Branch-and-Bound Strategies; 18.5 Application to a Flowshop Scheduling Problem; 18.6 Dynamic ProgrammingChap. 19 Local Search 19.1 Introduction; 19.2 Problem 1: The TSP; 19.3 Problem 2: Minimum-Cost Survivable Networks; 19.4 Problem 3: Topology of Offshore Natural Gas Pipeline Systems; 19.5 Problem 4: Uniform Graph Partitioning; 19.6 General Issues in Local Search; 19.7 The Geometry of Local Search; 19.8 An Example of a Large Minimal Exact Neighborhood; 19.9 The Complexity of Exact Local Search for the TSPAll chapters have problem sets and notes and references.As can be seen, this book has a mighty amount of information, and it is amazingly well-explained. Of course, you need a firm grasp of your linear algebra, and some knowledge of very elementary calc./real analysis and graph theory (although most of the graph theory needed, technically speaking, is supplied in an appendix). You don't even really need to know a programming language, since the authors use a "pidgin algol," explained in yet another appendix, for most of the algorithm stuff; all it takes is an orderly thought process to follow it.Despite the book's age, it mostly holds up very well in terms of topics and presentation. In the preface to the Dover edition, the authors briefly discuss some more current topics not dealt with in the text and make some (probably also out of date!) referrals for those wishing to "catch up." All in all, this book is a great value both as a text and a reference.

I had this book on my shelf for two years before taking a serious look at it, and only wish I had read it much earlier in life. Christos Papadimitriou has written quite a gem! On one hand this book serves as a good introduction to combinatorial optimization algorithms, in that it provides a flawless introduction to the simplex algorithm, linear and integer programming, and search techniques such as Branch-and-Bound and dynamic programming. On another, it serves as a good reference for many graph-theoretic algorithms. But most importantly Papadimitriou and Steiglitz seem to be on a quest to understand why some problems, such as Minimum Path or Matching, have efficient solutions, while others, such as Traveling Salesman, do not. And in doing so they end up providing the reader with a big picture behind algorithms and complexity, and the connection between optimization problems and complexity.After reading this and Papadimitriou's "Introduction to Computational Complexity" (which I also highly recommend), I now consider him one of the best at conveying complex ideas in a way that rarely confuses the reader. I also had the priviledge of attending one of his talks on complexity, and he seems just as effusive and transparent as a lecturer as he does a writer. Ah, for once I bought a Dover book that did not disappoint.

One could buy this book for different reasons: interests in combinatorial optimization, of course; interests in what Papadimitriou has to say, since his thoughts on this subject are definitely invaluable; perhaps the price is a good reason alone.Whatever the reason, however, I think that would be a rare event to remain duped.I was preparing my exam in Computability and Complexity when I first used it. I've been wonderfully surprised by the amount of definitions, algorithms, concepts I've found in this book. I think one could use this book for a simple course on Algorithms, on Computability and/or Complexity, on the whole Combinatorial Optimization, and the book would be always and costantly useful.The chapters on algorithms and complexity, or those on NP completeness have proved to be gems. The chapters on Approximation and Local Search are great, and they feature a bunch of detailed and excellent quality stuff (e.g. there is a detailed treatment of Christofides' algorithm to approximate the TSP, that is quite an idiosyncratic topic).All in all, a very great book, with a value exponentially greater than the very insignificant price.

The book is good. The only issue I have is the Kindle e-book for it. The text is great since there are real text, hence scalable and can reflow when I rotate my tablet. However, the equations are just too tiny to read. I have similiar problem with other ebooks I ordered - e.g. Scattered Data Interpolation.I wish the equation are not just a tiny image and is done probably - e.g. using Latex.

This is an excellent reference on the subject. The book methodically presents proofs for everything. It's a bit dense though and it might be a good idea to accompany this with something more introductory on some of its topics (graphs problems, linear programming etc), such as Cormen's book on algorithms.

This book was a present for my son , who is a student (undergraduate studies ) in the School of electrical and computer engineering in NTUA, that is the same technical university in which was student years ago , and I think professor for a little time , the writer Christos H. Papadimitriou . My son's first impressions is - despite the fact it is a little dated - about an excellent book . Recommended for all those interested in specialized knowledge,in this topic of science.

It is a classical and introduction-level book about combinatorial optimization. Although the book is published in 1998, the contents are still useful for current readers who would like to get further understanding of optimization techniques.

N'est absolument pas dépassé et sa lecture reste valable

Muito bom

Wonderful text on combinatorial optimization. The treatment of linear optimization alone is worth the price of admission. I like the unifying point of view of various combinatorial algorithms in a primal-dual framework. Unifying frameworks are always insightful to add to your arsenal of insights.

La calidad del libro no me gusto, parece foto copiado

Ho sostenuto un esame. È stato utile