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Efficient conflict driven learning in a boolean satisfiability solver

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TLDR
This paper generalizes various conflict driven learning strategies in terms of different partitioning schemes of the implication graph to re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes.
Abstract
One of the most important features of current state-of-the-art SAT solvers is the use of conflict based backtracking and learning techniques. In this paper, we generalize various conflict driven learning strategies in terms of different partitioning schemes of the implication graph. We re-examine the learning techniques used in various SAT solvers and propose an array of new learning schemes. Extensive experiments with real world examples show that the best performing new learning scheme has at least a 2/spl times/ speedup compared with learning schemes employed in state-of-the-art SAT solvers.

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Dissertation

Hybrid optimization of vehicle routing problems

Edward Lam
TL;DR: This dissertation presents two applications relevant to industry and develops a fully hybrid method, named Branch-and-Check with Explanations, that unifies linear programming, constraint programming and Boolean satisfiability for solving a classical vehicle routing problem.
Proceedings ArticleDOI

Adding a LBD-based rewarding mechanism in branching heuristic for SAT solvers

TL;DR: A new decision strategy is proposed — Rvar_LBD — extension of the rewarding mechanism by rewarding more variables that yield are involved in learnt clauses with small Literal Block Distance value, and implemented as part of Glucose3.0 solver.

An application of constraint programming and Boolean satisfiability solving techniques to variants of the resource-constrained project scheduling problem

TL;DR: In this article, a planungsaufgabe das ressourcenbeschrankte Projektplanungs problem (RCPSP) is genannt.
Dissertation

Solver tuning with genetic algorithms

Hu Xu
TL;DR: The self-learning genetic algorithm, which suggests or predicts a suitable solver configuration for test instances by learning from train instances, is proposed in this thesis, which demonstrates how genetic algorithms are implemented and adapted to aid in parameter selection for constraint solvers.

On Algorithms and Complexity for Sets with Cardinality Constraints PSPACE and PTIME Logics for Program Analysis

TL;DR: A nondeterministic polynomial-time algorithm for reducing the satisfiability of sets with symbolic cardinalities to constraints on constant car dinalities and a system of rewriting rules for enforcing certain consistency pr operties of these constraints are given and shown how to extract complete information from constraints in normal form.
References
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Book

Computers and Intractability: A Guide to the Theory of NP-Completeness

TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book

Introduction to Algorithms

TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Book

Genetic Algorithms

Journal ArticleDOI

Tabu Search—Part II

TL;DR: The elements of staged search and structured move sets are characterized, which bear on the issue of finiteness, and new dynamic strategies for managing tabu lists are introduced, allowing fuller exploitation of underlying evaluation functions.