SatEx: A Web-based Framework for SAT Experimentation

Simon, Laurent; Chatalic, Philippe

doi:10.1016/s1571-0653(04)00318-x

Cited by 25 publications

(21 citation statements)

References 11 publications

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“…It offers a general framework for problem solving and planning [7]. The performance results of the algorithms were taken from [13]. The results are from runs of the algorithm on 1,303 instances.…”

Section: Applicationmentioning

confidence: 99%

“…The choice of this value did not have a significant impact on the results. The set of all available instances from [13] is denoted as I. The set of all instances that are solvable by at least one available algorithm is denoted as I S .…”

Section: Applicationmentioning

confidence: 99%

“…To illustrate the motivation for this work, Figure 1 shows the relative performance of two SAT algorithms from the Sat-Ex collection [13]. It is obvious that each one of the algorithms outperforms the other on a significant number of instances.…”

Section: Introductionmentioning

confidence: 99%

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Learning parallel portfolios of algorithms

Petrik

Zilberstein

2006

Ann Math Artif Intell

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A wide range of combinatorial optimization algorithms have been developed for complex reasoning tasks. Frequently, no single algorithm outperforms all the others. This has raised interest in leveraging the performance of a collection of algorithms to improve performance. We show how to accomplish this using a Parallel Portfolio of Algorithms (PPA). A PPA is a collection of diverse algorithms for solving a single problem, all running concurrently on a single processor until a solution is produced. The performance of the portfolio may be controlled by assigning different shares of processor time to each algorithm. We present an effective method for finding a PPA in which the share of processor time allocated to each algorithm is fixed. Finding the optimal static schedule is shown to be an NP-complete problem for a general class of utility functions. We present bounds on the performance of the PPA over random instances and evaluate the performance empirically on a collection of 23 state-of-theart SAT algorithms. The results show significant performance gains over the fastest individual algorithm in the collection.

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Section: Applicationmentioning

confidence: 99%

Section: Applicationmentioning

confidence: 99%

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Learning parallel portfolios of algorithms

Petrik

Zilberstein

2006

Ann Math Artif Intell

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“…This ranking used in the SatEx web site [18] allows us to give a picture of solver performances in a very simple but meaningful way. It is not obvious to rank solvers on a basis of a penalty time when the status (SAT or UNSAT) of some benchmarks are not known.…”

Section: Rankingà La Satexmentioning

confidence: 99%

Fifty-Five Solvers in Vancouver: The SAT 2004 Competition

Berre

Simon

2005

Theory and Applications of Satisfiability Testing

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Abstract. For the third consecutive year, a SAT competition was organized as a joint event with the SAT conference. With 55 solvers from 25 author groups, the competition was a clear success. One of the noticeable facts from the 2004 competition is the superiority of incomplete solvers on satisfiable random k-SAT benchmarks. It can also be pointed out that the complete solvers awarded this year, namely Zchaff, jerusat1.3, satzoo-1.02, kncfsand march-eq, participated in the SAT 2003 competition (or at least former versions of those solvers). This paper is not reporting exhaustive competition results, already available in details online, but rather focuses on some remarkable results derived from the competition dataset.The SAT 2004 competition is ending a 3-year take-off period that attracted new SAT researchers and provided many new benchmarks and solvers to the community. The good participation rate of this year (despite the addition of the anti-blackbox rule) establishes the competition as an awaited yearly event. Some new directions for a new way of thinking about the competition are discussed at the end of the paper.

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“…algorithm [9], is known to be in practice vastly superior to DP. For example, in experimental data [20], a version of DP utilizing modern heuristics for computing good elimination orders performed much worse than DPLL based algorithms. Its behavior on the "jhn" family of problems was typical.…”

Section: Introductionmentioning

confidence: 99%