Unsatisfied Variables in Local Search

Unsatisfied Variables in Local Search (1995)

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by Ian P. Gent , Toby Walsh

Venue:	Hybrid Problems, Hybrid Solutions. IOS
Citations:	43 - 2 self

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Datum	Value	Source
TITLE	Unsatisfied Variables in Local Search	user correction - Legacy Corrections
AUTHOR NAME	Ian P. Gent	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of AI Mechanized Reasoning Group; University of Edinburgh IRST, Trento &; 80 South Bridge, Edinburgh DIST	SVM HeaderParse 0.2
AUTHOR ADDR	University of Genoa,; United Kingdom Italy	SVM HeaderParse 0.1
AUTHOR NAME	Toby Walsh	SVM HeaderParse 0.1
AUTHOR AFFIL	Department of AI Mechanized Reasoning Group; University of Edinburgh IRST, Trento &; 80 South Bridge, Edinburgh DIST	SVM HeaderParse 0.2
AUTHOR ADDR	University of Genoa,; United Kingdom Italy	SVM HeaderParse 0.1
ABSTRACT	Several local search algorithms for propositional satisfiability have been proposed which can solve hard random problems beyond the range of conventional backtracking procedures. In this paper, we explore the impact of focusing search in these procedures on the "unsatisfied variables"; that is, those variables which appear in clauses which are not yet satisfied. For random problems, we show that such a focus reduces the sensitivity to input parameters. We also observe a simple scaling law in performance. For non-random problems, we show that whilst this focus can improve performance, many problems remain difficult. We speculate that such problems will remain hard for local search unless constraint propagation techniques can be combined with hill-climbing. 1 Introduction Local search is often surprisingly effective as a semi-decision procedure for many NPhard problems. For example, Gsat, a greedy random hill-climbing procedure for propositional satisfiability (or SAT) is very good at ...	user correction - Legacy Corrections
YEAR	1995	INFERENCE
VENUE	Hybrid Problems, Hybrid Solutions. IOS	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	73--85	INFERENCE
CITATIONS	17 found	ParsCit 1.0