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Computational Combinatorial Optimization : Optimal or Provably Near-Optimal Solutions / edited by Michael Jünger, Denis Naddef.
LIBRA Q341 .P7 2004
Available from offsite location
- Format:
- Book
- Series:
- Computer Science (Springer-11645)
- Lecture notes in computer science 0302-9743 ; 2241.
- Lecture Notes in Computer Science, 0302-9743 ; 2241
- Language:
- English
- Subjects (All):
- Mathematical optimization.
- Computer science--Mathematics.
- Computer science.
- Algorithms.
- Information technology.
- Business--Data processing.
- Business.
- Data structures (Computer science).
- Combinatorial analysis.
- Optimization.
- Discrete Mathematics in Computer Science.
- Algorithm Analysis and Problem Complexity.
- IT in Business.
- Data Structures.
- Combinatorics.
- Local Subjects:
- Optimization.
- Discrete Mathematics in Computer Science.
- Algorithm Analysis and Problem Complexity.
- IT in Business.
- Data Structures.
- Combinatorics.
- Physical Description:
- 1 online resource (X, 310 pages).
- Edition:
- First edition 2001.
- Contained In:
- Springer eBooks
- Place of Publication:
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.
- System Details:
- text file PDF
- Summary:
- This tutorial contains written versions of seven lectures on Computational Combinatorial Optimization given by leading members of the optimization community. The lectures introduce modern combinatorial optimization techniques, with an emphasis on branch and cut algorithms and Lagrangian relaxation approaches. Polyhedral combinatorics as the mathematical backbone of successful algorithms are covered from many perspectives, in particular, polyhedral projection and lifting techniques and the importance of modeling are extensively discussed. Applications to prominent combinatorial optimization problems, e.g., in production and transport planning, are treated in many places; in particular, the book contains a state-of-the-art account of the most successful techniques for solving the traveling salesman problem to optimality.
- Contents:
- General Mixed Integer Programming: Computational Issues for Branch-and-Cut Algorithms
- Projection and Lifting in Combinatorial Optimization
- Mathematical Programming Models and Formulations for Deterministic Production Planning Problems
- Lagrangian Relaxation
- Branch-and-Cut Algorithms for Combinatorial Optimization and Their Implementation in ABACUS
- Branch, Cut, and Price: Sequential and Parallel
- TSP Cuts Which Do Not Conform to the Template Paradigm.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-540-45586-8
- 9783540455868
- Access Restriction:
- Restricted for use by site license.
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