Advances in high energy physics have created the need to increase computational capacity. Project HEPGAME was composed to address this challenge. One of the issues is that numerical integration of expressions of current interest have millions of terms and takes weeks to compute. We have investigated ways to simplify these expressions, using Horner schemes and common subexpression elimination. Our approach applies MCTS, a search procedure that has been successful in AI. We use it to find near-optimal Horner schemes. Although MCTS finds better solutions, this approach gives rise to two further challenges. (1) MCTS (with UCT) introduces a constant, @math that governs the balance between exploration and exploitation. This constant has to be tuned manually. (2) There should be more guided exploration at the bottom of the tree, since the current approach reduces the quality of the solution towards the end of the expression. We investigate NMCS (Nested Monte Carlo Search) to address both issues, but find that NMCS is computationally unfeasible for our problem. Then, we modify the MCTS formula by introducing a dynamic exploration-exploitation parameter @math that decreases linearly with the iteration number. Consequently, we provide a performance analysis. We observe that a variable @math solves our domain: it yields more exploration at the bottom and as a result the tuning problem has been simplified. The region in @math for which good values are found is increased by more than a tenfold. This result encourages us to continue our research to solve other prominent problems in High Energy Physics.
The Boolean Satisfiability problem, or SAT, is a central problem in symbolic logic and computational complexity. Ever since Cook's seminal work @cite_5 , finding efficient solvers for SAT has driven much progress in computational logic and combinatorial optimization. MCTS has been quite successful in adversary search and optimization @cite_19 . In the current work, we discuss the application of MCTS to expression simplification. Curiously, we are not aware of many other works, with the notable exception of @cite_0 .
@cite_19 @cite_5 @cite_0
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Boolean satisfiability is probably the most studied of the combinatorial optimization search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO. Instances of the Boolean satisfiability problem (SAT) arise in many areas of circuit design and verification. These instances are typically constructed from some human-designed artifact, and thus are likely to possess much inherent symmetry and sparsity. Previous work[4] has shown that exploiting symmetries results in vastly reduced SAT solver run times, often with the search for the symmetries themselves dominating the total SAT solving time. Our contribution is twofold. First, we dissect the algorithms behind the venerable NAUTY[9] package, particularly the partition refinement procedure responsible for the majority of search space pruning as well as the majority of run time overhead. Second, we present a new symmetry-detection tool, SAUCY, which outperforms NAUTY by several orders of magnitude on the large, structured CNF formulas generated from typical EDA problems. Many practically important combinatorial problems can be efficiently reduced to a problem of Boolean satisfiability (SAT). Therefore, the implementation of distributed algorithms for solving SAT problems is of great importance. In this article we describe a technology for organizing desktop grid, which is meant for solving SAT problems. This technology was implemented in the form of a volunteer computing project SAT@home based on a popular BOINC platform. Generalised Satisfiability Problems (or Boolean Constraint Satisfaction Problems), introduced by Schaefer in 1978, are a general class of problem which allow the systematic study of the complexity of satisfiability problems with different types of constraints. In 1979, Valiant introduced the complexity class parity P, the problem of counting the number of solutions to NP problems modulo two. Others have since considered the question of counting modulo other integers. We give a dichotomy theorem for the complexity of counting the number of solutions to Generalised Satisfiability Problems modulo integers. This follows from an earlier result of Creignou and Hermann which gave a counting dichotomy for these types of problem, and the dichotomy itself is almost identical. Specifically, counting the number of solutions to a Generalised Satisfiability Problem can be done in polynomial time if all the relations are affine. Otherwise, except for one special case with k = 2, it is #_kP-complete.