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
Synthesis and Optimization of Digital Circuits
Synthesis and Optimization of Digital Circuits
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
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Combinatorial optimization problems that may be expressed as 'Boolean constraint satisfaction problems' (BCSPs) are being solved by different communities under different formulations and in different formats. If results of experimentation are reported, these can be seldom compared and replicated. We propose a pragmatic approach to reconcile these issues: (1) use the familiar LP model that naturally expresses the constraints as well as the goals of the optimization task to formulate an optimization instance, (2) assemble and translate a number of hard-to-solve instances from different domains into the .lpx format parsed by at least two BCSP solvers: lp_solve in public domain, and cplex, (3) expose the intrinsic variability of BCSP solvers by constructing instance isomorphs as an equivalence class of randomized replicas of a reference instance; (4) use isomorph classes for the design of reproducible experiments with BCSP solvers that includes performance testing hypotheses; (5) release (on the web) all data sets, reported results, and software utilities used to prepare the data, invoke experiments, and post-process the results.