Add to ORKGThe IntSat procedure [Nieuwenhuis 2014] is a complete method for Integer Linear Programming (ILP) based on conflict-driven constraint learning, extending similar ideas used in propositional satisfiability solving (SAT). The aim of this project is, restricted to the Pseudo-Boolean ILP case, to extend this method with new inprocessing techniques, proving the associated correctness and completeness properties, developing data structures and algorithms for their implementation, and providing a careful and extensive experimental assessment for them
We introduce branch and infer, a unifying framework for integer linear programming and finite domain...
The paper describes a method to solve an ILP by describing whether an approximated integer solution ...
Integer Linear Programming (ILP) is among the most successful and general paradigms for solving comp...
The IntSat procedure [Nieuwenhuis 2014] is a complete method for Integer Linear Programming (ILP) ba...
Abstract. Conflict-Driven Clause-Learning (CDCL) SAT solvers can automat-ically solve very large rea...
Conflict-Driven Clause-Learning (CDCL) SAT solvers can automatically solve very large real-world pro...
Conflict-Driven Clause Learning (CDCL) SAT solvers can automatically solve very large real-world pro...
In this paper we describe two Propositional Satisfiability-based algorithms for solving 0-1 integer ...
Pseudo-Boolean problems generalize SAT problems by allowing linear constraints and a linear objectiv...
In this paper we describe a new branch and bound algorithm for solving 0-1 integer linear programs (...
Abstract. Linear integer constraints are one of the most important constraints in combinatorial prob...
In this paper we describe two Propositional Satisfiability-based algorithms for solving 0-1 integer ...
The purpose of this introductory chapter is to provide the basic concepts behind Constraint Program...
We introduce a new Integer Linear Programming (ILP) approach for solving Integer Programming (IP) pr...
Integer programming (discrete optimization) is best used for solving problems involving discrete, wh...
We introduce branch and infer, a unifying framework for integer linear programming and finite domain...
The paper describes a method to solve an ILP by describing whether an approximated integer solution ...
Integer Linear Programming (ILP) is among the most successful and general paradigms for solving comp...
The IntSat procedure [Nieuwenhuis 2014] is a complete method for Integer Linear Programming (ILP) ba...
Abstract. Conflict-Driven Clause-Learning (CDCL) SAT solvers can automat-ically solve very large rea...
Conflict-Driven Clause-Learning (CDCL) SAT solvers can automatically solve very large real-world pro...
Conflict-Driven Clause Learning (CDCL) SAT solvers can automatically solve very large real-world pro...
In this paper we describe two Propositional Satisfiability-based algorithms for solving 0-1 integer ...
Pseudo-Boolean problems generalize SAT problems by allowing linear constraints and a linear objectiv...
In this paper we describe a new branch and bound algorithm for solving 0-1 integer linear programs (...
Abstract. Linear integer constraints are one of the most important constraints in combinatorial prob...
In this paper we describe two Propositional Satisfiability-based algorithms for solving 0-1 integer ...
The purpose of this introductory chapter is to provide the basic concepts behind Constraint Program...
We introduce a new Integer Linear Programming (ILP) approach for solving Integer Programming (IP) pr...
Integer programming (discrete optimization) is best used for solving problems involving discrete, wh...
We introduce branch and infer, a unifying framework for integer linear programming and finite domain...
The paper describes a method to solve an ILP by describing whether an approximated integer solution ...
Integer Linear Programming (ILP) is among the most successful and general paradigms for solving comp...