• Corpus ID: 8540521

A New Method for Solving Hard Satisfiability Problems

@inproceedings{Selman1992ANM,
  title={A New Method for Solving Hard Satisfiability Problems},
  author={Bart Selman and Hector J. Levesque and David G. Mitchell},
  booktitle={AAAI},
  year={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 approaches such as the Davis-Putnam procedure or resolution. We also show that GSAT can solve structured satisfiability problems quickly. In particular, we solve encodings of graph coloring problems, N… 
A New Met
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
A New Met Bart Selman
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
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TLDR
This work presents three strategies that dramatically improve GSAT's performance on formulas with a high degree of asymmetry, thereby significantly extending the applicability of the GSAT algorithm.
Solving Hard Combinatorial Problems with GSAT - A Case Study
  • H. Hoos
  • Mathematics, Computer Science
    KI
  • 1996
TLDR
Whether hard combinatorial problems such as the Hamiltonian circuit problem HCP can be practically solved by transformation to the propositional satisfiability problem (SAT) and application of fast universal SAT-algorithms like GSAT to the transformed problem instances is investigated.
An Empirical Study of Greedy Local Search for Satisfiability Testing
TLDR
This paper describes the space traversed by GSAT, and discusses two general, domain-independent extensions that dramatically improve GSAT's performance on structured problems: the use of clause weights, and a way to average in near-solutions when initializing lhe procedure before each try.
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GSAT is a randomized local search procedure for solving propositional satisfiability problems. GSAT can solve hard, randomly generated problems that are an order of magnitude larger than those that
Complete Boolean Satisfiability Solving Algorithms Based on Local Search
TLDR
A translation method and three effective complete SAT solving algorithms based on the characterization of Model RB structure are proposed that translate clauses into a graph with exclusive sets and relative sets and determine search order using vertex weights and clique in the graph.
Solving Problems with Hard and Soft Constraints Using a Stochastic Algorithm for MAX-SAT
Stochastic local search is an effective technique for solving certain classes of large, hard propositional satisfiability problems, including propositional encodings of problems such as circuit
A Graph-Based Method for Improving GSAT
TLDR
An improvement to GSAT that is sensitive to the problem's structure is presented and results of experiments are presented showing that this new algorithm outperforms regular GSAT on sparse networks whose cycle-cutset size is bounded by 30% of the nodes.
Solving Linear Pseudo-Boolean Constraint Problems with Local Search
TLDR
It is shown that domain-independent local search for satisfiability (Walksat) can be generalized to handle systems of linear pseudo-Boolean (0-1 integer) constraints, a representation that is widely used in operations research.
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