Overview
- Authors:
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Martin Grötschel
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Konrad-Zuse-Zentrum für Informationstechnik Berlin, Berlin, Germany
Fachbereich Mathematik, Technische Universität Berlin, Berlin, Germany
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László Lovász
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Department of Computer Science, Eötvös Loránd University, Budapest, Hungary
Department of Mathematics, Yale University, New Haven, USA
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Alexander Schrijver
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CWI (Center for Mathematics and Computer Science), Amsterdam, The Netherlands
Department of Mathematics, University of Amsterdam, Amsterdam, The Netherlands
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Table of contents (11 chapters)
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 1-20
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 21-45
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 46-63
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 64-101
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 102-132
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 133-156
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 157-196
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 197-224
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 225-271
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 272-303
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- Martin Grötschel, László Lovász, Alexander Schrijver
Pages 304-329
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Back Matter
Pages 331-363
About this book
Since the publication of the first edition of our book, geometric algorithms and combinatorial optimization have kept growing at the same fast pace as before. Nevertheless, we do not feel that the ongoing research has made this book outdated. Rather, it seems that many of the new results build on the models, algorithms, and theorems presented here. For instance, the celebrated Dyer-Frieze-Kannan algorithm for approximating the volume of a convex body is based on the oracle model of convex bodies and uses the ellipsoid method as a preprocessing technique. The polynomial time equivalence of optimization, separation, and membership has become a commonly employed tool in the study of the complexity of combinatorial optimization problems and in the newly developing field of computational convexity. Implementations of the basis reduction algorithm can be found in various computer algebra software systems. On the other hand, several of the open problems discussed in the first edition are still unsolved. For example, there are still no combinatorial polynomial time algorithms known for minimizing a submodular function or finding a maximum clique in a perfect graph. Moreover, despite the success of the interior point methods for the solution of explicitly given linear programs there is still no method known that solves implicitly given linear programs, such as those described in this book, and that is both practically and theoretically efficient. In particular, it is not known how to adapt interior point methods to such linear programs.
Authors and Affiliations
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Konrad-Zuse-Zentrum für Informationstechnik Berlin, Berlin, Germany
Martin Grötschel
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Fachbereich Mathematik, Technische Universität Berlin, Berlin, Germany
Martin Grötschel
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Department of Computer Science, Eötvös Loránd University, Budapest, Hungary
László Lovász
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Department of Mathematics, Yale University, New Haven, USA
László Lovász
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CWI (Center for Mathematics and Computer Science), Amsterdam, The Netherlands
Alexander Schrijver
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Department of Mathematics, University of Amsterdam, Amsterdam, The Netherlands
Alexander Schrijver