Geometric algorithms and combinatorial optimization / Martin Grotschel, László Lovász, Alexander Schrijver

Grötschel, Martin. Schrijver, A. Lovász, László

ISBN 10: 038713624X / ISBN 13: 9780387136240

Published by Berlin, Heidelberg : Springer-Verlag, 1994

About this Item

Library copy - library marks remain. Very good copy in the original title-blocked cloth. Slight suggestion only of dust-dulling to the spine bands and panel edges. Remains particularly well-preserved overall; tight, bright, clean and strong. Physical description: XII, 362p. 23 illus. Notes: Bibliographic Level Mode of Issuance: MonographIncludes bibliographical references and index. Contents: 0. Mathematical Preliminaries -- 0.1 Linear Algebra and Linear Programming -- 0.2 Graph Theory -- 1. Complexity, Oracles, and Numerical Computation -- 1.1 Complexity Theory: P and NP -- 1.2 Oracles -- 1.3 Approximation and Computation of Numbers -- 1.4 Pivoting and Related Procedures -- 2. Algorithmic Aspects of Convex Sets: Formulation of the Problems -- 2.1 Basic Algorithmic Problems for Convex Sets -- 2.2 Nondeterministic Decision Problems for Convex Sets -- 3. The Ellipsoid Method -- 3.1 Geometric Background and an Informal Description -- 3.2 The Central-Cut Ellipsoid Method -- 3.3 The Shallow-Cut Ellipsoid Method -- 4. Algorithms for Convex Bodies -- 4.1 Summary of Results -- 4.2 Optimization from Separation -- 4.3 Optimization from Membership -- 4.4 Equivalence of the Basic Problems -- 4.5 Some Negative Results -- 4.6 Further Algorithmic Problems for Convex Bodies -- 4.7 Operations on Convex Bodies -- 5. Diophantine Approximation and Basis Reduction -- 5.1 Continued Fractions -- 5.2 Simultaneous Diophantine Approximation: Formulation of the Problems -- 5.3 Basis Reduction in Lattices -- 5.4 More on Lattice Algorithms -- 6. Rational Polyhedra -- 6.1 Optimization over Polyhedra: A Preview -- 6.2 Complexity of Rational Polyhedra -- 6.3 Weak and Strong Problems -- 6.4 Equivalence of Strong Optimization and Separation -- 6.5 Further Problems for Polyhedra -- 6.6 Strongly Polynomial Algorithms -- 6.7 Integer Programming in Bounded Dimension -- 7. Combinatorial Optimization: Some Basic Examples. -- 7.1 Flows and Cuts -- 7.2 Arborescences -- 7.3 Matching -- 7.4 Edge Coloring -- 7.5 Matroids -- 7.6 Subset Sums -- 7.7 Concluding Remarks -- 8. Combinatorial Optimization: A Tour d Horizon -- 8.1 Blocking Hypergraphs and Polyhedra -- 8.2 Problems on Bipartite Graphs -- 8.3 Flows, Paths, Chains, and Cuts -- 8.4 Trees, Branchings, and Rooted and Directed Cuts -- 8.5 Matchings, Odd Cuts, and Generalizations -- 8.6 Multicommodity Flows -- 9. Stable Sets in Graphs -- 9.1 Odd Circuit Constraints and t-Perfect Graphs -- 9.2 Clique Constraints and Perfect Graphs -- 9.3 Orthonormal Representations -- 9.4 Coloring Perfect Graphs -- 9.5 More Algorithmic Results on Stable Sets -- 10. Submodular Functions -- 10.1 Submodular Functions and Polymatroids -- 10.2 Algorithms for Polymatroids and Submodular Functions -- 10.3 Submodular Functions on Lattice, Intersecting, and Crossing Families -- 10.4 Odd Submodular Function Minimization and Extensions -- References -- Notation Index -- Author Index. Subjects: Combinatorial geometry.Geometry of numbers.Mathematical optimization.Combinatorial geometry.Geometry of numbers.Mathematical optimization.Programming (Mathematics)Combinatorial geometry.Geometry of numbers.Mathematical optimization.Programming (Mathematics)Mathematics. Combinatorial analysis.Combinatorial analysis.Mathematics.Combinatorics. 1 Kg.

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Bibliographic Details

• Title: Geometric algorithms and combinatorial optimization / Martin Grotschel, László Lovász, Alexander Schrijver
• Publisher: Berlin, Heidelberg : Springer-Verlag
• Publication Date: 1994
• Language: English
• ISBN 10: 038713624X
• ISBN 13: 9780387136240
• Binding: Hardcover
• Edition: First Edition.
• Weight: 1 kilogram

About this title

Synopsis

This book develops geometric techniques for proving the polynomial time solvability of problems in convexity theory, geometry, and - in particular - combinatorial optimization. It offers a unifying approach based on two fundamental geometric algorithms: - the ellipsoid method for finding a point in a convex set and - the basis reduction method for point lattices. The ellipsoid method was used by Khachiyan to show the polynomial time solvability of linear programming. The basis reduction method yields a polynomial time procedure for certain diophantine approximation problems. A combination of these techniques makes it possible to show the polynomial time solvability of many questions concerning poyhedra - for instance, of linear programming problems having possibly exponentially many inequalities. Utilizing results from polyhedral combinatorics, it provides short proofs of the poynomial time solvability of many combinatiorial optimization problems. For a number of these problems, the geometric algorithms discussed in this book are the only techniques known to derive polynomial time solvability. This book is a continuation and extension of previous research of the authors for which they received the Fulkerson Prize, awarded by the Mathematical Programming Society and the American Mathematical Society.