615 research outputs found
Preprocessing and Stochastic Local Search in Maximum Satisfiability
Problems which ask to compute an optimal solution to its instances are called optimization problems. The maximum satisfiability (MaxSAT) problem is a well-studied combinatorial optimization problem with many applications in domains such as cancer therapy design, electronic markets, hardware debugging and routing. Many problems, including the aforementioned ones, can be encoded in MaxSAT. Thus MaxSAT serves as a general optimization paradigm and therefore advances in MaxSAT algorithms translate to advances in solving other problems.
In this thesis, we analyze the effects of MaxSAT preprocessing, the process of reformulating the input instance prior to solving, on the perceived costs of solutions during search. We show that after preprocessing most MaxSAT solvers may misinterpret the costs of non-optimal solutions. Many MaxSAT algorithms use the found non-optimal solutions in guiding the search for solutions and so the misinterpretation of costs may misguide the search.
Towards remedying this issue, we introduce and study the concept of locally minimal solutions. We show that for some of the central preprocessing techniques for MaxSAT, the perceived cost of a locally minimal solution to a preprocessed instance equals the cost of the corresponding reconstructed solution to the original instance.
We develop a stochastic local search algorithm for MaxSAT, called LMS-SLS, that is prepended with a preprocessor and that searches over locally minimal solutions. We implement LMS-SLS and analyze the performance of its different components, particularly the effects of preprocessing and computing locally minimal solutions, and also compare LMS-SLS with the state-of-the-art SLS solver SATLike for MaxSAT.
The Configurable SAT Solver Challenge (CSSC)
It is well known that different solution strategies work well for different
types of instances of hard combinatorial problems. As a consequence, most
solvers for the propositional satisfiability problem (SAT) expose parameters
that allow them to be customized to a particular family of instances. In the
international SAT competition series, these parameters are ignored: solvers are
run using a single default parameter setting (supplied by the authors) for all
benchmark instances in a given track. While this competition format rewards
solvers with robust default settings, it does not reflect the situation faced
by a practitioner who only cares about performance on one particular
application and can invest some time into tuning solver parameters for this
application. The new Configurable SAT Solver Competition (CSSC) compares
solvers in this latter setting, scoring each solver by the performance it
achieved after a fully automated configuration step. This article describes the
CSSC in more detail, and reports the results obtained in its two instantiations
so far, CSSC 2013 and 2014
Multilevel techniques and learning automata for the Maximum Satisfiability (MAXSAT) problem
The Maximum Satisfiability (MAXSAT) Problem is a propositional logic and an optimization
based problem that has great importance in the theoretical and practical domain. In
the recent years MAXSAT has risen great interest in the industry. Example problems from
the industry that can be encoded as MAXSAT problems are circuit design and debugging,
hardware verification, bioinformatics and scheduling. These kind of problems often tend
to be large and increase exponentially with the problem size, and therefore algorithms for
solving such problems incorporate different techniques and methods to solve the problems
in a smart and efficient manner.
In this thesis we introduce a range of algorithms that extend the well-known Stochastic
Local Search (SLS) algorithm called WalkSAT. WalkSAT is extended with the multilevel
paradigm and Learning Automata. The multilevel paradigm is a technique that splits large
and difficult problems into smaller problems. These problems are expectedly less complex
and therefore easier to solve. Learning Automata are a branch of machine learning that
can be seen as a decision-making entity that is employed in an unknown environment.
Through feedback from the environment the Learning Automata try to learn the optimal
actions.
The core of this thesis is the observations and findings of how these dissimilar techniques affect
the performance and behaviour of WalkSAT when solving industrial MAXSAT problem
instances. Through extensive experiments our results confirm that combining multilevel
techniques and Learning Automata with WalkSAT, separately and together, give promising
results. We compare these composite algorithms with WalkSAT on selected industrial
MAXSAT problems throughout the thesis, and show that all these composite algorithms
perform better than WalkSAT
Stochastic local search: a state-of-the-art review
The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search algorithms is given. Finally, the last part of the paper present and discuss some of the most latest trends in application of stochastic local search algorithms in machine learning, data mining and some other areas of science and engineering. We conclude with a discussion on capabilities and limitations of stochastic local search algorithms
Contingent planning under uncertainty via stochastic satisfiability
We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSAT) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSAT problems and the solution of stochastic planning problems. This is the first work extending the planning-as-satisfiability paradigm to stochastic domains. Our planner, ZANDER, can solve arbitrary, goal-oriented, finite-horizon partially observable Markov decision processes (POMDPs). An empirical study comparing ZANDER to seven other leading planners shows that its performance is competitive on a range of problems. © 2003 Elsevier Science B.V. All rights reserved
Boosting local search thanks to {CDCL}
International audienceIn this paper, a novel hybrid and complete approach for propositional satisfiability, called SAT HYS (Sat Hybrid Solver), is introduced. It efficiently combines the strength of both local search and CDCL based SAT solvers. Considering the consistent partial assignment under construction by the CDCL SAT solver, local search is used to extend it to a model of the Boolean formula, while the CDCL component is used by the local search one as a strategy to escape from a local minimum. Additionally, both solvers heavily cooperate thanks to relevant information gathered during search. Experimentations on SAT instances taken from the last competitions demonstrate the efficiency and the robustness of our hybrid solver with respect to the state-of-the-art CDCL based, local search and hybrid SAT solvers
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