Research Projects

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

04/2009

TUD Haushalt

keine

http://

Nein

Nein

Nein

Satisfiability checking, SAT-solving, Component-based SAT-solver

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

04/2009

TUD Haushalt

keine

http://

Nein

Nein

Nein

Satisfiability checking, SAT-solving, Component-based SAT-solver

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

04/2009

TUD Haushalt

keine

http://

Nein

Nein

Nein

Satisfiability checking, SAT-solving, Component-based SAT-solver

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

04/2009

TUD Haushalt

keine

http://

Nein

Nein

Nein

Satisfiability checking, SAT-solving, Component-based SAT-solver

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

04/2009

TUD Haushalt

keine

http://

Nein

Nein

Nein

Satisfiability checking, SAT-solving, Component-based SAT-solver

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

The goal is a generic SAT-solver that serves as a basis for various solver\'s configurations via combination of solver\'s components. It offers great flexibility in deriving different SAT-solver instances from available components and existing parameters. This is of importance in many facets, e.g. in optimizing solvers for a particular domain by automatically tuning solver\'s parameters and in constructing portfolio-based solvers. We consider components from classical DPLL-based to CDCL-based solvers, from principal components (e.g. search, unit propagation, decision heuristics, backtracking) to secondary components (e.g. learning, clause forgetting heuristics, restart heuristics, preprocessing).

04/2009

TUD Haushalt

keine

http://

Nein

Nein

Nein

Satisfiability checking, SAT-solving, Component-based SAT-solver