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SATzilla: Portfoliobased Algorithm Selection for SAT
"... It has been widely observed that there is no single “dominant ” SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a perinst ..."
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Cited by 91 (16 self)
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It has been widely observed that there is no single “dominant ” SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a perinstance basis. Building on previous work, we describe SATzilla, an automated approach for constructing perinstance algorithm portfolios for SAT that use socalled empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of our SATzilla portfolios has been independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition. 1.
Incomplete Algorithms
, 2008
"... An incomplete method for solving the propositional satisfiability problem (or a general constraint satisfaction problem) is one that does not provide the guarantee that it will eventually either report a satisfying assignment or declare that the given formula is unsatisfiable. In practice, most such ..."
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Cited by 5 (0 self)
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An incomplete method for solving the propositional satisfiability problem (or a general constraint satisfaction problem) is one that does not provide the guarantee that it will eventually either report a satisfying assignment or declare that the given formula is unsatisfiable. In practice, most such methods are biased towards the satisfiable side: they are typically run with a preset resource limit, after which they either produce a valid solution or report failure; they never declare the formula to be unsatisfiable. These are the kind of algorithms we will discuss in this chapter. In complexity theory terms, such algorithms are referred to as having onesided error. In principle, an incomplete algorithm could instead be biased towards the unsatisfiable side, always providing proofs of unsatisfiability but failing to find solutions to some satisfiable instances, or be incomplete with respect to both satisfiable and unsatisfiable instances (and thus have twosided error). Unlike systematic solvers often based on an exhaustive branching and backtracking search, incomplete methods are generally based on stochastic local search,
Reactive Search Optimization: Learning while Optimizing
"... The final purpose of Reactive Search Optimization (RSO) is to simplify the life for the final user of optimization. While researchers enjoy designing algorithms, testing alternatives, tuning parameters and choosing solution schemes — in fact this is part of their daily life — the final users ’ inter ..."
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Cited by 4 (2 self)
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The final purpose of Reactive Search Optimization (RSO) is to simplify the life for the final user of optimization. While researchers enjoy designing algorithms, testing alternatives, tuning parameters and choosing solution schemes — in fact this is part of their daily life — the final users ’ interests are different: solving a problem in the
REACTIVE SEARCH FOR MAXSAT: DIVERSIFICATION BIAS PROPERTIES WITH PROHIBITIONS AND PENALTIES
, 2007
"... Reactive search for MAXSAT: diversificationbias properties with prohibitions and penalties ..."
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Reactive search for MAXSAT: diversificationbias properties with prohibitions and penalties
Importance of Variables Semantic in CNF Encoding of Cardinality Constraints
"... In the satisfiability domain, it is wellknown that a SAT algorithm may solve a problem instance easily and another instance hardly, whilst these two instances are equivalent CNF encodings of the original problem. Moreover, different algorithms may disagree on which encoding makes the problem easier ..."
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In the satisfiability domain, it is wellknown that a SAT algorithm may solve a problem instance easily and another instance hardly, whilst these two instances are equivalent CNF encodings of the original problem. Moreover, different algorithms may disagree on which encoding makes the problem easier to solve. In this paper, we focus on the CNF encoding of cardinality constraints, which states that exactly k propositional variables in a given set are assigned to true. We demonstrate the importance of the semantics of the SAT variables in the encoding of this constraint. We implement several variants of the CNF encoding in which the close semantic variables are grouped. We then examine these new encodings on problems generated from diagnosis of discreteevent system. Our results demonstrate that both stochastic and systematic SAT algorithms can now solve most of the problem instances, which were unreachable before (Grastien et al. 2007). These results also indicate that, on average cases, there is an encoding that suits well both SLS and DPLL algorithms.
Weight Redistribution for Unweighted MAXSAT
"... Abstract. Many realworld problems are overconstrained and require search techniques adapted to optimising cost functions rather than searching for consistency. This makes the MAXSAT problem an important area of research for the satisfiability (SAT) community. In this study we perform an empirical ..."
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Abstract. Many realworld problems are overconstrained and require search techniques adapted to optimising cost functions rather than searching for consistency. This makes the MAXSAT problem an important area of research for the satisfiability (SAT) community. In this study we perform an empirical analysis of several of the best performing SAT local search techniques in the domain of unweighted MAXSAT. In particular, we test two of the most recently developed SAT clause weight redistribution algorithms, DDFW and DDFW +, against three more wellknown techniques (RSAPS, AdaptNovelty + and PAWS). Based on an empirical study across a range of previously studied problems we conclude that DDFW is the most promising algorithm in terms of robust average performance. 1