Inspired by AND/OR search spaces for graphical models recently introduced, we propose to augment Ordered Decision Diagrams with AND nodes, in order to capture function decomposition structure. This yields AND/OR multivalued decision diagram (AOMDD) which compiles a constraint network into a canonical form that supports polynomial time queries such as solution counting, solution enumeration or equivalence of constraint networks. We provide a compilation algorithm based on Variable Elimination for assembling an AOMDD for a constraint network starting from the AOMDDs for its constraints. The algorithm uses the APPLY operator which combines two AOMDDs by a given operation. This guarantees the complexity upper bound for the compilation time and ...
Combinatorial optimization problems require selecting the best solution from a discrete (albeit ofte...
Abstract. The typical constraint store transmits a limited amount of information because it consists...
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete optimizati...
Abstract. The paper is an overview of a recently developed compilation data structure for graphical ...
Compiling graphical models has recently been under intense investigation, especially for prob-abilis...
Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propo...
Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propo...
Inspired by the recently introduced framework of AND/OR search spaces for graphical mod-els, we prop...
International audienceSeveral branching heuristics for compiling in a top-down fashion finite-domain...
International audienceSeveral branching heuristics for compiling in a top-down fashion finite-domain...
International audienceSeveral branching heuristics for compiling in a top-down fashion finite-domain...
Constraint programming is a well known efficient programming paradigm sometimes called smart brute-f...
We present an incremental refinement algorithm for approximate compilation of constraint satisfactio...
Constraint programming is a declarative way of modeling and solving optimization and satisfiability ...
A general n-ary constraint is usually represented explicitly as a set of its solution tuples, which ...
Combinatorial optimization problems require selecting the best solution from a discrete (albeit ofte...
Abstract. The typical constraint store transmits a limited amount of information because it consists...
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete optimizati...
Abstract. The paper is an overview of a recently developed compilation data structure for graphical ...
Compiling graphical models has recently been under intense investigation, especially for prob-abilis...
Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propo...
Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propo...
Inspired by the recently introduced framework of AND/OR search spaces for graphical mod-els, we prop...
International audienceSeveral branching heuristics for compiling in a top-down fashion finite-domain...
International audienceSeveral branching heuristics for compiling in a top-down fashion finite-domain...
International audienceSeveral branching heuristics for compiling in a top-down fashion finite-domain...
Constraint programming is a well known efficient programming paradigm sometimes called smart brute-f...
We present an incremental refinement algorithm for approximate compilation of constraint satisfactio...
Constraint programming is a declarative way of modeling and solving optimization and satisfiability ...
A general n-ary constraint is usually represented explicitly as a set of its solution tuples, which ...
Combinatorial optimization problems require selecting the best solution from a discrete (albeit ofte...
Abstract. The typical constraint store transmits a limited amount of information because it consists...
Decision diagrams are an increasingly important tool in cutting-edge solvers for discrete optimizati...