Fifty-Five Solvers in Vancouver: The SAT 2004 Competition

Berre, Daniel Le; Simon, Laurent

doi:10.1007/11527695_25

Cited by 31 publications

(19 citation statements)

References 22 publications

(16 reference statements)

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“…Minisat was enhanced by a restart strategy that was found to be optimal for this solver in [2]. We used eight publicly available benchmark families: sat04-ind-goldberg03-hard eq check [6] (henceforth, abbreviated to ug), sat04-ind-maris03-gripper [6] (mm), sat04-ind-velevvliw unsat 2.0 [9] (uv2), SAT-Race TS 1 [10] For each solver, we compared the following four versions, applying: (1) no shrinking; (2) the base version of shrinking, corresponding to Eureka's version of shrinking (recall from Section 2 that Eureka's shrinking algorithm is largely similar to Chaff's: its shrinking condition is based on clause length and the sorting scheme picks variables in descending order of decision levels); (3) the base version, modified by applying activity ordering; (4) the base version, modified by using the decision-level-based shrinking condition. Table 1 provides some statistics regarding the benchmark families as well as Eureka's results.…”

Section: Resultsmentioning

confidence: 99%

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Assignment Stack Shrinking

Nadel

Ryvchin

2010

Theory and Applications of Satisfiability Testing – SAT 2010

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Abstract. Assignment stack shrinking is a technique that is intended to speed up the performance of modern complete SAT solvers. Shrinking was shown to be efficient in SAT'04 competition winners Jerusat and Chaff. However, existing studies lack the details of the shrinking algorithm. In addition, shrinking's performance was not tested in conjunction with the most modern techniques. This paper provides a detailed description of the shrinking algorithm and proposes two new heursitics for it. We show that using shrinking is critical for solving well-known industrial benchmark families with the latest versions of Minisat and Eureka.

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Section: Resultsmentioning

confidence: 99%

“…We concentrate on zchaff rand 's version of shrinking, since it was shown to be more useful in [5], and also performed better in the SAT'04 competition [6]. Suppose Chaff encounters a conflict.…”

Section: Algorithmic Details and New Heuristicsmentioning

confidence: 99%

Assignment Stack Shrinking

Nadel

Ryvchin

2010

Theory and Applications of Satisfiability Testing – SAT 2010

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“…For the experimental results the three algorithms were implemented on the basis of the PB solver MINISAT+ [13] or SAT solver MINISAT [14], respectively, which participated very successful in the past SAT Competition [18] and PB Evaluation [19]. Through using the same PB solver as the basis for all three algorithms, we have the chance for a fair comparison on the basis of runtime.…”

Section: Resultsmentioning

confidence: 99%

“…This is done by removing that domain and adding new domains whereas an improvement in at least one dimension has to be achieved (line [12][13][14][15]. Concluding, the set D is cleaned up in which sub-domains of other domains are removed (line [17][18][19].…”

Section: Algorithmmentioning

confidence: 99%

Solving Multi-objective Pseudo-Boolean Problems

Lukasiewycz

Glaß

Haubelt

et al.

Theory and Applications of Satisfiability Testing – SAT 2007

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Abstract. Integer Linear Programs are widely used in areas such as routing problems, scheduling analysis and optimization, logic synthesis, and partitioning problems. As many of these problems have a Boolean nature, i.e., the variables are restricted to 0 and 1, so called Pseudo-Boolean solvers have been proposed. They are mostly based on SAT solvers which took continuous improvements over the past years. However, Pseudo-Boolean solvers are only able to optimize a single linear function while fulfilling several constraints. Unfortunately many realworld optimization problems have multiple objective functions which are often conflicting and have to be optimized simultaneously, resulting in general in a set of optimal solutions. As a consequence, a single-objective Pseudo-Boolean solver will not be able to find this set of optimal solutions. As a remedy, we propose three different algorithms for solving multi-objective Pseudo-Boolean problems. Our experimental results will show the applicability of these algorithms on the basis of several test cases.

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“…Most work in the field has focused just on performance-oriented quality metrics for solvers. For example, the basic measure used in both the most recent (at the time of writing) SAT Competition and SMT Competition was simply the pair of the number of benchmarks solved and running time to solve them, compared in the natural lexicographic order (for the competitions mentioned, see, e.g., [1,3]). While the SAT competition has also experimented recently with more complex measures, they are also centered on performance.…”

mentioning

confidence: 99%

Exploring Predictability of SAT/SMT Solvers

Brummayer

Oe²,

Stump³

EPiC Series in Computing

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This paper seeks to explore the predictability of SAT and SMT solvers in response to different kinds of changes to benchmarks. We consider both semantics-preserving and possibly semantics-modifying transformations, and provide preliminary data about solver predictability. We also propose carrying learned theory lemmas over from an original run to runs on similar benchmarks, and show the benefits of this idea as a heuristic for improving predictability of SMT solvers. MotivationSAT and SMT (Satisfiability Modulo Theories) solvers have enjoyed tremendous performance improvements in the past ten years, increasing the automated-reasoning power available for applications like algorithmic verification, combinatorial design, planning, and others (e.g., [7,5,4]). Most work in the field has focused just on performance-oriented quality metrics for solvers. For example, the basic measure used in both the most recent (at the time of writing) SAT Competition and SMT Competition was simply the pair of the number of benchmarks solved and running time to solve them, compared in the natural lexicographic order (for the competitions mentioned, see, e.g., [1,3]). While the SAT competition has also experimented recently with more complex measures, they are also centered on performance.In this paper, we propose another property to consider when evaluating solvers, namely predictability. While users certainly require and benefit from improvements to raw performance, anecdotal evidence from end users of solvers suggests that in some cases unpredictability is at least as significant a concern. For example, Steve Miller, Principal Software Engineer in the Advanced Technology Center of Rockwell Collins, reported in his keynote presentation at Midwest Verification Day 2009 that unpredictability is a significant issue for his team in incorporating SAT/SMT solvers into their verification workflow. Unpredictability is a problem because a small change to a model can lead to an enormous change in the amount of time to solve the resulting verification condition. If the amount of time is enormously longer, the verification may become infeasible or unacceptably delayed. If it is enormously shorter, engineers may doubt the result, questioning if an error elsewhere in the workflow has led to such different system behavior. It may improve the usability of such solvers to sacrifice a modest amount of performance for improved predictability.In this paper we provide a preliminary study of predictability of SAT (Section 2) and SMT (Section 3) solvers under small mutations of standard benchmarks. We use the standard deviation of solver times on a collection of mutants as a measure of variability. In the case of SMT solvers, we also propose and study a technique for heuristically improving predictability, by carrying over a selection of learned theory lemmas from the original run of the solver to the runs on the mutants.

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Fifty-Five Solvers in Vancouver: The SAT 2004 Competition

Cited by 31 publications

References 22 publications

Assignment Stack Shrinking

Assignment Stack Shrinking

Solving Multi-objective Pseudo-Boolean Problems

Exploring Predictability of SAT/SMT Solvers

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