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57
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.
Learning conjunctions of Horn clauses
 In Proceedings of the 31st Annual Symposium on Foundations of Computer Science
, 1990
"... Abstract. An algorithm is presented for learning the class of Boolean formulas that are expressible as conjunctions of Horn clauses. (A Horn clause is a disjunction of literals, all but at most one of which is a negated variable.) The algorithm uses equivalence queries and membership queries to prod ..."
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Cited by 112 (16 self)
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Abstract. An algorithm is presented for learning the class of Boolean formulas that are expressible as conjunctions of Horn clauses. (A Horn clause is a disjunction of literals, all but at most one of which is a negated variable.) The algorithm uses equivalence queries and membership queries to produce a formula that is logically equivalent to the unknown formula to be learned. The amount of time used by the algorithm is polynomial in the number of variables and the number of clauses in the unknown formula.
On relating time and space to size and depth
 SIAM Journal on Computing
, 1977
"... Abstract. Turing machine space complexity is related to circuit depth complexity. The relationship complements the known connection between Turing machine time and circuit size, thus enabling us to expose the related nature of some important open problems concerning Turing machine and circuit comple ..."
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Cited by 97 (1 self)
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Abstract. Turing machine space complexity is related to circuit depth complexity. The relationship complements the known connection between Turing machine time and circuit size, thus enabling us to expose the related nature of some important open problems concerning Turing machine and circuit complexity. We are also able to show some connection between Turing machine complexity and arithmetic complexity.
The complexity of relational query languages (extended abstract
 In Proceedings of the fourteenth annual ACM symposium on Theory of computing (STOC ’82
, 1982
"... Two complexity measures for query languages are proposed. Data complexity is the complexity of evaluating a query in the language as a function of the size of the database, and expression complexity is the complexity of ewduating a query in the language as a function of the size of the expression de ..."
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Cited by 67 (0 self)
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Two complexity measures for query languages are proposed. Data complexity is the complexity of evaluating a query in the language as a function of the size of the database, and expression complexity is the complexity of ewduating a query in the language as a function of the size of the expression defining the query. We study the data and expression complexity of logical langnages relational calculus and its extensions by transitive closure, fixpoint and second order existential quantification and algebraic languages relational algebra and its extensions by bounded and unbounded looping. The pattern which will bc shown is that the expression complexity of the investigated languages is one exponential higher then their data complexity, and for both types of complexity we show completeness in some complexity class. Research supported by a Weizrnann Postdoctoral Fellowship,
Models of Computation  Exploring the Power of Computing
"... Theoretical computer science treats any computational subject for which a good model can be created. Research on formal models of computation was initiated in the 1930s and 1940s by Turing, Post, Kleene, Church, and others. In the 1950s and 1960s programming languages, language translators, and oper ..."
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Cited by 57 (7 self)
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Theoretical computer science treats any computational subject for which a good model can be created. Research on formal models of computation was initiated in the 1930s and 1940s by Turing, Post, Kleene, Church, and others. In the 1950s and 1960s programming languages, language translators, and operating systems were under development and therefore became both the subject and basis for a great deal of theoretical work. The power of computers of this period was limited by slow processors and small amounts of memory, and thus theories (models, algorithms, and analysis) were developed to explore the efficient use of computers as well as the inherent complexity of problems. The former subject is known today as algorithms and data structures, the latter computational complexity. The focus of theoretical computer scientists in the 1960s on languages is reflected in the first textbook on the subject, Formal Languages and Their Relation to Automata by John Hopcroft and Jeffrey Ullman. This influential book led to the creation of many languagecentered theoretical computer science courses; many introductory theory courses today continue to reflect the content of this book and the interests of theoreticians of the 1960s and early 1970s. Although
Separation of the Monotone NC Hierarchy
, 1999
"... We prove tight lower bounds, of up to n ffl , for the monotone depth of functions in monotoneP. As a result we achieve the separation of the following classes. 1. monotoneNC 6= monotoneP. 2. For every i 1, monotoneNC i 6= monotoneNC i+1 . 3. More generally: For any integer function D( ..."
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Cited by 35 (0 self)
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We prove tight lower bounds, of up to n ffl , for the monotone depth of functions in monotoneP. As a result we achieve the separation of the following classes. 1. monotoneNC 6= monotoneP. 2. For every i 1, monotoneNC i 6= monotoneNC i+1 . 3. More generally: For any integer function D(n), up to n ffl (for some ffl ? 0), we give an explicit example of a monotone Boolean function, that can be computed by polynomial size monotone Boolean circuits of depth D(n), but that cannot be computed by any (fanin 2) monotone Boolean circuits of depth less than Const \Delta D(n) (for some constant Const). Only a separation of monotoneNC 1 from monotoneNC 2 was previously known. Our argument is more general: we define a new class of communication complexity search problems, referred to below as DART games, and we prove a tight lower bound for the communication complexity of every member of this class. As a result we get lower bounds for the monotone depth of many functions. In...
Interconvertibility of Set Constraints and ContextFree Language Reachability
 In Proceedings of the ACM SIGPLAN Symposium on Partial Evaluation and SemanticsBased Program Manipulation
, 1996
"... We show the interconvertibility of contextfreelanguage reachability problems and a class of setconstraint problems: given a contextfreelanguage reachability problem, we show how to construct a setconstraint problem whose answer gives a solution to the reachability problem; given a setconstrai ..."
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Cited by 29 (1 self)
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We show the interconvertibility of contextfreelanguage reachability problems and a class of setconstraint problems: given a contextfreelanguage reachability problem, we show how to construct a setconstraint problem whose answer gives a solution to the reachability problem; given a setconstraint problem, we show how to construct a contextfreelanguage reachability problem whose answer gives a solution to the setconstraint problem. The interconvertibility of these two formalisms offers an conceptual advantage akin to the advantage gained from the interconvertibility of finitestate automata and regular expressions in formal language theory, namely, a problem can be formulated in whichever formalism is most natural. It also offers some insight into the "O(n³) bottleneck" for different types of programanalysis problems, and allows results previously obtained for contextfreelanguage reachability problems to be applied to setconstraint problems.
Interconvertibility of a Class of Set Constraints and ContextFreeLanguage Reachability
 TCS
, 1998
"... We show the interconvertibility of contextfreelanguage reachability problems and a class of setconstraint problems: given a contextfreelanguage reachability problem, we show how to construct a setconstraint problem whose answer gives a solution to the reachability problem; given a setconstra ..."
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Cited by 27 (2 self)
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We show the interconvertibility of contextfreelanguage reachability problems and a class of setconstraint problems: given a contextfreelanguage reachability problem, we show how to construct a setconstraint problem whose answer gives a solution to the reachability problem; given a setconstraint problem, we show how to construct a contextfreelanguage reachability problem whose answer gives a solution to the setconstraint problem. The interconvertibility of these two formalisms offers an conceptual advantage akin to the advantage gained from the interconvertibility of finitestate automata and regular expressions in formal language theory, namely, a problem can be formulated in whichever formalism is most natural. It also offers some insight into the "O(n ) bottleneck" for different types of programanalysis problems and allows results previously obtained for contextfreelanguage reachability problems to be applied to setconstraint problems and vice versa.
Finding AlmostSatisfying Assignments
 In Proceedings of the 30th Annual ACM Symposium on Theory of Computing
, 1997
"... Schaefer showed, long ago, that there are, essentially, only three nontrivial classes of conjunctive Boolean formulae (or constraint satisfaction problems) for which satis ability can be decided in polynomial time (assuming P 6= NP ). These three classes are LIN, 2SAT and HORNSAT. LIN is the c ..."
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Cited by 25 (3 self)
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Schaefer showed, long ago, that there are, essentially, only three nontrivial classes of conjunctive Boolean formulae (or constraint satisfaction problems) for which satis ability can be decided in polynomial time (assuming P 6= NP ). These three classes are LIN, 2SAT and HORNSAT. LIN is the constraint satisfaction problem in which all the constraints are linear equations modulo 2. 2SAT is the constraint satisfaction problem in which all the constraints are disjunctions of at most two variables or their negations. HORNSAT is the constraint satisfaction problem in which all the constraints are Horn clauses, i.e., disjunctions containing at most one negated variable.
Optimal strategies for testing nondeterministic systems
 In ISSTA’04, volume 29 of Software Engineering Notes
, 2004
"... This paper deals with testing of nondeterministic software systems. We assume that a model of the nondeterministic system is given by a directed graph with two kind of vertices: states and choice points. Choice points represent the nondeterministic behaviour of the implementation under test (IUT). E ..."
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Cited by 19 (6 self)
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This paper deals with testing of nondeterministic software systems. We assume that a model of the nondeterministic system is given by a directed graph with two kind of vertices: states and choice points. Choice points represent the nondeterministic behaviour of the implementation under test (IUT). Edges represent transitions. They have costs and probabilities. Test case generation in this setting amounts to generation of a game strategy. The two players are the testing tool (TT) and the IUT. The game explores the graph. The TT leads the IUT by selecting an edge at the state vertices. At the choice points the control goes to the IUT. A game strategy decides which edge should be taken by the TT in each state. This paper presents three novel algorithms 1) to determine an optimal strategy for the bounded reachability game, where optimality means maximizing the probability to reach any of the given final states from a given start state while at the same time minimizing the costs of traversal; 2) to determine a winning strategy for the bounded reachability game, which guarantees that given final vertices are reached, regardless how the IUT reacts; 3) to determine a fast converging edge covering strategy, which guarantees that the probability to cover all edges quickly converges to 1 if TT follows the strategy.