Integer and combinatorial optimization
Integer and combinatorial optimization
Design of experiments in BDD variable ordering: lessons learned
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
Logic Synthesis and Verification Algorithms
Logic Synthesis and Verification Algorithms
The Approximability of Constraint Satisfaction Problems
SIAM Journal on Computing
On SAT Instance Classes and a Method for Reliable Performance Experiments with SAT Solvers
Annals of Mathematics and Artificial Intelligence
Effective bounding techniques for solving unate and binate covering problems
Proceedings of the 42nd annual Design Automation Conference
Heuristics for a bidding problem
Computers and Operations Research
High-contrast algorithm behavior: observation, conjecture, and experimental design
ecs'07 Experimental computer science on Experimental computer science
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Careful ranking of multiple solvers with timeouts and ties
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Statistical methodology for comparison of SAT solvers
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
A survey of the satisfiability-problems solving algorithms
International Journal of Advanced Intelligence Paradigms
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Combinatorial optimization problems expressed as Boolean constraint satisfaction problems (BCSPs) arise in several contexts, ranging from the classical unate set-packing problems to the binate minimum cover problems, including the Haplotype Inference by Pure Parsimony (HIPP) problem. These problems are being solved under different formulations and in different formats. Results of experiments that are reported can be seldom compared and replicated. This paper is not about 'the best BCSP solver'. Rather, it is a case study of how the scientific method can be applied to comparing the performance of not only BCSP solvers but also other solvers that address NP-hard problems. The approach is founded on two premises: (1) the introduction of instance isomorphs as families of equivalence classes, based on randomized replicas of a given reference instance, and (2) the use of isomorph classes for the design of reproducible experiments with BCSP solvers that includes performance testing hypotheses. We introduce a number of BCSP reference instances from different domains, generate isomorph classes and use various versions of cplex to characterize the solver performance and the isomorph classes themselves. This methodology may make it easier to (1) reliably improve the performance of combinatorial solvers and, (2) report results of experiments under the proposed schema.