Computational Combinatorial Optimization: Optimal or Provably Near-Optimal Solutions

by Michael Jïnger (Editor), Denis Naddef (Editor)

Overview

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.

Product Details

ISBN-13:	9783540428770
Publisher:	Springer Berlin Heidelberg
Publication date:	12/18/2001
Series:	Lecture Notes in Computer Science , #2241
Edition description:	2001
Pages:	310
Product dimensions:	8.50(w) x 10.98(h) x (d)

Table of 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.