ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
Theoretical Computer Science
Volume 348, Issues 2-3, 8 December 2005, Pages 357-365
Automata, Languages and Programming: Algorithms and Complexity (ICALP-A 2004)
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Purchase PDF (215 K)

 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
View Record in Scopus
 
doi:10.1016/j.tcs.2005.09.023    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier B.V. All rights reserved.

A new algorithm for optimal 2-constraint satisfaction and its implicationsstar, open

Ryan WilliamsE-mail The Corresponding Author

Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Available online 3 October 2005.

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Abstract

We present a novel method for exactly solving (in fact, counting solutions to) general constraint satisfaction optimization with at most two variables per constraint (e.g. MAX-2-CSP and MIN-2-CSP), which gives the first exponential improvement over the trivial algorithm. More precisely, the runtime bound is a constant factor improvement in the base of the exponent: the algorithm can count the number of optima in MAX-2-SAT and MAX-CUT instances in O(m32ωn/3) time, where ω<2.376 is the matrix product exponent over a ring. When the constraints have arbitrary weights, there is a (1+ε)-approximation with roughly the same runtime, modulo polynomial factors. Our construction shows that improvement in the runtime exponent of either k-clique solution (even when k=3) or matrix multiplication over GF(2) would improve the runtime exponent for solving 2-CSP optimization.

Our approach also yields connections between the complexity of some (polynomial time) high-dimensional search problems and some NP-hard problems. For example, if there are sufficiently faster algorithms for computing the diameter of n points in 1, then there is an (2-ε)n algorithm for MAX-LIN. These results may be construed as either lower bounds on the high-dimensional problems, or hope that better algorithms exist for the corresponding hard problems.

Keywords: Exact algorithms; Constraint satisfaction; MAX-2-SAT; MAX-CUT


Theoretical Computer Science
Volume 348, Issues 2-3, 8 December 2005, Pages 357-365
Automata, Languages and Programming: Algorithms and Complexity (ICALP-A 2004)
 
Home
Browse
My Settings
Alerts
Help
Elsevier.com (Opens new window)
About ScienceDirect  |  Contact Us  |  Information for Advertisers  |  Terms & Conditions  |  Privacy Policy
Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.