Abstract
This chapter outlines some successful frameworks for combinatorial optimization. It is meant as a brief introduction into terminology and outlines the basic principles of the frameworks which are applied in the subsequent case studies. The three frameworks that will be discussed, integer linear programming (ILP), finite domain constraint programming (CP) and local search are well established, comprise of a variety of techniques, and many successful applications have been reported. ILP and CP can be considered the state-of-the-art of general-purpose optimization methods, whereas local search should be seen as an approach that can be tailored to many different optimization problems by adapting its conceptual components to the respective problem context. We also discuss successful local search methods for solving propositional satisfiability problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Editor information
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(1999). Frameworks for Combinatorial Optimization. In: Walser, J.P. (eds) Integer Optimization by Local Search. Lecture Notes in Computer Science(), vol 1637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48369-1_2
Download citation
DOI: https://doi.org/10.1007/3-540-48369-1_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66367-6
Online ISBN: 978-3-540-48369-4
eBook Packages: Springer Book Archive