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Author:   Oliver Kullmann
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2009 ed.
Volume:   5584
Dimensions:   Width: 15.50cm , Height: 3.00cm , Length: 23.50cm
Weight:   0.842kg
ISBN:  

9783642027765

ISBN 10:   3642027768
Pages:   540
Publication Date:   19 June 2009
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Invited Talks.- SAT Modulo Theories: Enhancing SAT with Special-Purpose Algorithms.- Symbolic Techniques in Propositional Satisfiability Solving.- Applications of SAT.- Efficiently Calculating Evolutionary Tree Measures Using SAT.- Finding Lean Induced Cycles in Binary Hypercubes.- Finding Efficient Circuits Using SAT-Solvers.- Encoding Treewidth into SAT.- Complexity Theory.- The Complexity of Reasoning for Fragments of Default Logic.- Does Advice Help to Prove Propositional Tautologies?.- Structures for SAT.- Backdoors in the Context of Learning.- Solving SAT for CNF Formulas with a One-Sided Restriction on Variable Occurrences.- On Some Aspects of Mixed Horn Formulas.- Variable Influences in Conjunctive Normal Forms.- Resolution and SAT.- Clause-Learning Algorithms with Many Restarts and Bounded-Width Resolution.- An Exponential Lower Bound for Width-Restricted Clause Learning.- Improved Conflict-Clause Minimization Leads to Improved Propositional Proof Traces.- Boundary Points and Resolution.- Translations to CNF.- Sequential Encodings from Max-CSP into Partial Max-SAT.- Cardinality Networks and Their Applications.- New Encodings of Pseudo-Boolean Constraints into CNF.- Efficient Term-ITE Conversion for Satisfiability Modulo Theories.- Techniques for Conflict-Driven SAT Solvers.- On-the-Fly Clause Improvement.- Dynamic Symmetry Breaking by Simulating Zykov Contraction.- Minimizing Learned Clauses.- Extending SAT Solvers to Cryptographic Problems.- Solving SAT by Local Search.- Improving Variable Selection Process in Stochastic Local Search for Propositional Satisfiability.- A Theoretical Analysis of Search in GSAT.- The Parameterized Complexity of k-Flip Local Search for SAT and MAX SAT.- Hybrid SAT Solvers.- A Novel Approach to Combine a SLS- and a DPLL-Solverfor the Satisfiability Problem.- Building a Hybrid SAT Solver via Conflict-Driven, Look-Ahead and XOR Reasoning Techniques.- Automatic Adaption of SAT Solvers.- Restart Strategy Selection Using Machine Learning Techniques.- Instance-Based Selection of Policies for SAT Solvers.- Width-Based Restart Policies for Clause-Learning Satisfiability Solvers.- Problem-Sensitive Restart Heuristics for the DPLL Procedure.- Stochastic Approaches to SAT Solving.- (1,2)-QSAT: A Good Candidate for Understanding Phase Transitions Mechanisms.- VARSAT: Integrating Novel Probabilistic Inference Techniques with DPLL Search.- QBFs and Their Representations.- Resolution and Expressiveness of Subclasses of Quantified Boolean Formulas and Circuits.- A Compact Representation for Syntactic Dependencies in QBFs.- Beyond CNF: A Circuit-Based QBF Solver.- Optimisation Algorithms.- Solving (Weighted) Partial MaxSAT through Satisfiability Testing.- Nonlinear Pseudo-Boolean Optimization: Relaxation or Propagation?.- Relaxed DPLL Search for MaxSAT.- Branch and Bound for Boolean Optimization and the Generation of Optimality Certificates.- Exploiting Cycle Structures in Max-SAT.- Generalizing Core-Guided Max-SAT.- Algorithms for Weighted Boolean Optimization.- Distributed and Parallel Solving.- PaQuBE: Distributed QBF Solving with Advanced Knowledge Sharing.- c-sat: A Parallel SAT Solver for Clusters.

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