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13
Improvements To Propositional Satisfiability Search Algorithms
, 1995
"... ... quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1.3 U1, POSIT can solve hard random 400variable 3SAT problems in about 2 hours on the average. In general, it can solve hard nvariable ..."
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Cited by 161 (0 self)
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... quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1.3 U1, POSIT can solve hard random 400variable 3SAT problems in about 2 hours on the average. In general, it can solve hard nvariable random 3SAT problems with search trees of size O(2 n=18:7 ). In addition to justifying these claims, this dissertation describes the most significant achievements of other researchers in this area, and discusses all of the widely known general techniques for speeding up SAT search algorithms. It should be useful to anyone interested in NPcomplete problems or combinatorial optimization in general, and it should be particularly useful to researchers in either Artificial Intelligence or Operations Research.
Algorithms for the Satisfiability (SAT) Problem: A Survey
 DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1996
"... . The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computeraided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, compute ..."
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Cited by 127 (3 self)
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. The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computeraided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the sat...
A Fast Parallel SATSolver  Efficient Workload Balancing
, 1994
"... We present a fast parallel SATsolver on a message based MIMD machine. The input formula is dynamically divided into disjoint subformulas. Small subformulas are solved by a fast sequential SATsolver running on every processor, which is based on the DavisPutnam procedure with a special heuristic fo ..."
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Cited by 47 (3 self)
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We present a fast parallel SATsolver on a message based MIMD machine. The input formula is dynamically divided into disjoint subformulas. Small subformulas are solved by a fast sequential SATsolver running on every processor, which is based on the DavisPutnam procedure with a special heuristic for variable selection. The algorithm uses optimized data structures to modify boolean formulas. Additionally efficient workload balancing algorithms are used, to achieve a uniform distribution of workload among the processors. We consider the communication network topologies ddimensional processor grid and linear processor array. Tests with up to 256 processors have shown very good efficiencyvalues (> 0.95).
Average Case Results for Satisfiability Algorithms Under the Random Clause Width Model
 Annals of Mathematics and Artificial Intelligence
, 1995
"... In the probabilistic analysis of algorithms for the Satisfiability problem, the randomclausewidth model is one of the most popular for generating random instances. This model is parameterized and it is not difficult to show that virtually the entire parameter space is covered by a collection of ..."
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Cited by 9 (1 self)
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In the probabilistic analysis of algorithms for the Satisfiability problem, the randomclausewidth model is one of the most popular for generating random instances. This model is parameterized and it is not difficult to show that virtually the entire parameter space is covered by a collection of polynomial time algorithms that find solutions to random instances with probability tending to 1 as instance size increases. But finding a collection of polynomial average time algorithms that cover the parameter space has proved much harder and such results have spanned approximately ten years. However, it can now be said that virtually the entire parameter space is covered by polynomial average time algorithms. This paper relates dominant, exploitable properties of random formulas over the parameter space to mechanisms of polynomial average time algorithms. The probabilistic discussion of such properties is new; main averagecase results over the last ten years are reviewed. 1 Intr...
Backtracking and Probing
, 1993
"... : We analyze two algorithms for solving constraint satisfaction problems. One of these algorithms, Probe Order Backtracking, has an average running time much faster than any previously analyzed algorithm for problems where solutions are common. Probe Order Backtracking uses a probing assignment (a p ..."
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Cited by 6 (2 self)
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: We analyze two algorithms for solving constraint satisfaction problems. One of these algorithms, Probe Order Backtracking, has an average running time much faster than any previously analyzed algorithm for problems where solutions are common. Probe Order Backtracking uses a probing assignment (a preselected test assignment to unset variables) to help guide the search for a solution to a constraint satisfaction problem. If the problem is not satisfied when the unset variables are temporarily set to the probing assignment, the algorithm selects one of the relations that the probing assignment fails to satisfy and selects an unset variable from that relation. Then at each backtracking step it generates subproblems by setting the selected variable each possible way. It simplifies each subproblem, and tries the same technique on them. For random problems with v variables, t clauses, and probability p that a literal appears in a clause, the average time for Probe Order Backtracking is no m...
The Probability of Pure Literals
"... We describe an error in earlier probabilistic analyses of the pure literal heuristic as a procedure for solving kSAT . All probabilistic analyses are in the constant degree model in which a random instance C of kSAT consists of m clauses selected independently and uniformly (with replacement) from ..."
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Cited by 6 (1 self)
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We describe an error in earlier probabilistic analyses of the pure literal heuristic as a procedure for solving kSAT . All probabilistic analyses are in the constant degree model in which a random instance C of kSAT consists of m clauses selected independently and uniformly (with replacement) from the set of all kclauses over n variables. We provide a new analysis for k = 2. Specifically, we show with probability approaching 1 as m goes to 1 one can apply the pure literal rule repeatedly to a random instance of 2SAT until the number of clauses is "small" provided n=m ? 1. But if n=m ! 1, with probability approaching 1 if the pure literal rule is applied as much as possible, then at least m 1=5 clauses will remain. Keywords: 2SAT , constant degree model, DavisPutnam Procedure, pure literal (heuristic), probability of a pure literal 1 1
Probe Order Backtracking
, 1997
"... . The algorithm for constraintsatisfaction problems, Probe Order Backtracking, has an average running time much faster than any previously analyzed algorithm under conditions where solutions are common. The algorithm uses a probing assignment (a preselected test assignment to unset variables) to he ..."
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Cited by 4 (0 self)
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. The algorithm for constraintsatisfaction problems, Probe Order Backtracking, has an average running time much faster than any previously analyzed algorithm under conditions where solutions are common. The algorithm uses a probing assignment (a preselected test assignment to unset variables) to help guide the search for a solution. If the problem is not satisfied when the unset variables are temporarily set to the probing assignment, the algorithm selects one of the relations which is not satisfied by the probing assignment and selects an unset variable which a#ects the value of that relation. It then does a backtracking (splitting) step, where it generates subproblems by setting the selected variable each possible way. Each subproblem is simplified and then solved recursively. For random problems with v variables, t clauses, and probability p that a literal appears in a clause, the average time for Probe Order Backtracking is no more than v n when p # (ln t)/v plus lowerorder t...
Convergence Properties of Optimization Algorithms for the Satisfiability (SAT) Problem
 IEEE Trans. on Computers
, 1996
"... : The satisfiability (SAT) problem is a basic problem in computing theory. Presently, an active area of research on SAT problem is to design efficient optimization algorithms for finding a solution for a satisfiable CNF formula. A new formulation, the Universal SAT problem model, which transforms t ..."
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Cited by 2 (1 self)
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: The satisfiability (SAT) problem is a basic problem in computing theory. Presently, an active area of research on SAT problem is to design efficient optimization algorithms for finding a solution for a satisfiable CNF formula. A new formulation, the Universal SAT problem model, which transforms the SAT problem on Boolean space into an optimization problem on real space has been developed [31, 35, 34, 32]. Many optimization techniques, such as the steepest descent method, Newton's method, and the coordinate descent method, can be used to solve the Universal SAT problem. In this paper, we prove that, when the initial solution is sufficiently close to the optimal solution, the steepest descent method has a linear convergence ratio fi ! 1, Newton's method has a convergence ratio of order two, and the convergence ratio of the steepest descent method is approximately (1 \Gamma fi=m) for the Universal SAT problem with m variables. An algorithm based on the coordinate descent method for the...
Some Interesting Research Directions in Satisfiability
, 2000
"... Some reflections on past research in SAT algorithms is presented. Some of the more important goals of SAT research are stated and results given. Possible future SAT research topics are outlined. ..."
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Some reflections on past research in SAT algorithms is presented. Some of the more important goals of SAT research are stated and results given. Possible future SAT research topics are outlined.
Gedanken: A tool for pondering the tractability of correct program technology
, 1994
"... syntax of elementary languages in Gedanken . . . . . . . . . . . 129 7.1 Match counting algorithm for patterns over PC k . . . . . . . . . . . . . 157 8.1 log 2 speed of Model Graphs after elimination . . . . . . . . . . . . . . . 187 8.2 log 2 speedup of Model Graphs after elimination . . . . . . ..."
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syntax of elementary languages in Gedanken . . . . . . . . . . . 129 7.1 Match counting algorithm for patterns over PC k . . . . . . . . . . . . . 157 8.1 log 2 speed of Model Graphs after elimination . . . . . . . . . . . . . . . 187 8.2 log 2 speedup of Model Graphs after elimination . . . . . . . . . . . . . 188 8.3 log 2 speed of Model Graphs after invalidation . . . . . . . . . . . . . . . 188 8.4 log 2 speedup of Model Graphs after invalidation . . . . . . . . . . . . . 189 ix Chapter 1 Summary One goal of computer science has been to develop a tool T to aid a programmer in building a program P that satisfies a specification S by helping the programmer build a proof in some logic of programs L that shows that P satisfies S. S typically is a pair of propositions (#, #) such that, for an input x to P , #(x) # #(P (x)) when P is defined on x. # is called the precondition or assumption, and # is called the postcondition or assertion. The problem of finding a suitable logic L of programs and specifications and verification tool T may be generically referred to as the "FloydHoare problem", formulated around 1967 [Flo67, Hoa69]. Around 1977, Davis and Schwartz proposed an extension of the FloydHoare problem in which there are multiple assumptions and assertions, referring to the state of a program as execution passes through di#erent places # in the program [DS77, Sch77]. A placed proposition is then a pair (#, #), where # is either a line of a program or the name of a function. A placed proposition (#, #) holds when, if execution reaches # and the value of the variables X in P is V , then #(V ) is valid. A program with assumptions and assertions or praa is then a triple R = (P, E, F ) where the assumptions E and assertions F are sets of placed propositions. T...