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- Paul Beame, Henry A. Kautz, Ashish Sabharwal
- J. Artif. Intell. Res.
- 2004

Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant real-world problems, such as… (More)

- Carla P. Gomes, Jörg Hoffmann, Ashish Sabharwal, Bart Selman
- IJCAI
- 2007

We introduce a new technique for counting models of Boolean satisfiability problems. Our approach incorporates information obtained from sampling the solution space. Unlike previous approaches, our… (More)

- Carla P. Gomes, Henry A. Kautz, Ashish Sabharwal, Bart Selman
- Handbook of Knowledge Representation
- 2008

The past few years have seen an enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known algorithms, satisfiability… (More)

- Carla P. Gomes, Ashish Sabharwal, Bart Selman
- NIPS
- 2006

We propose a new technique for sampling the solutions of combinatorial problems in a near-uniform manner. We focus on problems specified as a Boolean formula, i.e., on SAT instances. Sampling for SAT… (More)

Algorithm portfolios aim to increase the robustness of our ability to solve problems efficiently. While recently proposed algorithm selection methods come ever closer to identifying the most… (More)

- Carla P. Gomes, Ashish Sabharwal, Bart Selman
- AAAI
- 2006

Model counting is the classical problem of computing the number of solutions of a given propositional formula. It vastly generalizes the NP-complete problem of propositional satisfiability, and hence… (More)

- Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
- NIPS
- 2013

We consider the problem of sampling from a probability distribution defined over a high-dimensional discrete set, specified for instance by a graphical model. We propose a sampling algorithm, called… (More)

There has been considerable interest in the identification of structural properties of combinatorial problems that lead to efficient algorithms for solving them. Some of these properties are “easily”… (More)

Different solution approaches for combinatorial problems often exhibit incomparable performance that depends on the concrete problem instance to be solved. Algorithm portfolios aim to combine the… (More)

Many combinatorial problems, such as car sequencing and rostering, feature sequence constraints, restricting the number of occurrences of certain values in every subsequence of a given width. To… (More)