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A New Method for Solving Hard Satisfiability Problems
 AAAI
, 1992
"... We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approac ..."
Abstract

Cited by 683 (21 self)
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We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the DavisPutnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, Nqueens, and Boolean induction. General application strategies and limitations of the approach are also discussed. GSAT is best viewed as a modelfinding procedure. Its good performance suggests that it may be advantageous to reformulate reasoning tasks that have traditionally been viewed as theoremproving problems as modelfinding tasks.
The NPcompleteness column: an ongoing guide
 Journal of Algorithms
, 1985
"... This is the nineteenth edition of a (usually) quarterly column that covers new developments in the theory of NPcompleteness. The presentation is modeled on that used by M. R. Garey and myself in our book ‘‘Computers and Intractability: A Guide to the Theory of NPCompleteness,’ ’ W. H. Freeman & Co ..."
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Cited by 188 (0 self)
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This is the nineteenth edition of a (usually) quarterly column that covers new developments in the theory of NPcompleteness. The presentation is modeled on that used by M. R. Garey and myself in our book ‘‘Computers and Intractability: A Guide to the Theory of NPCompleteness,’ ’ W. H. Freeman & Co., New York, 1979 (hereinafter referred to as ‘‘[G&J]’’; previous columns will be referred to by their dates). A background equivalent to that provided by [G&J] is assumed, and, when appropriate, crossreferences will be given to that book and the list of problems (NPcomplete and harder) presented there. Readers who have results they would like mentioned (NPhardness, PSPACEhardness, polynomialtimesolvability, etc.) or open problems they would like publicized, should
Computational Complexity, Protein Structure Prediction, and the Levinthal Paradox
 Computational Complexity Protein Structure Prediction and the Levinthal Paradox
, 1994
"... The task of determining the globally optimal (minimumenergy) conformation of a protein given its potentialenergy function is widely believed to require an amount of computer time that is exponential in the number of soft degrees of freedom in the protein. Conventional reasoning as to the exponenti ..."
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Cited by 19 (0 self)
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The task of determining the globally optimal (minimumenergy) conformation of a protein given its potentialenergy function is widely believed to require an amount of computer time that is exponential in the number of soft degrees of freedom in the protein. Conventional reasoning as to the exponential time complexity of this problem is fallaciousit is based solely on the size of the search spaceand for some variants of the proteinstructure prediction problem the conclusion is likely to be incorrect. Every problem in combinatorial optimization has an exponential number of candidate solutions, but many such problems can be solved by algorithms that do not require exponential time. We present a critical review of efforts to characterize rigorously the computational requirements of global potentialenergy minimization for a polypeptide chain that has a unique energy minimum corresponding to the native structure of the protein. An argument by Crippen (1975) demonstrated that an algor...