Integer and combinatorial optimization
Integer and combinatorial optimization
Mixed 0-1 programming by lift-and-project in a branch-and-cut framework
Management Science
GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Mixed logical-linear programming
Discrete Applied Mathematics - Special issue on the satisfiability problem and Boolean functions
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Tight Cooperation and Its Application in Piecewise Linear Optimization
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
SCIL - Symbolic Constraints in Integer Linear Programming
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Branch and Infer: a Unifying Framework for Integer and Finite Domain Constraint Programming
INFORMS Journal on Computing
Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems
INFORMS Journal on Computing
Cutting planes in integer and mixed integer programming
Discrete Applied Mathematics
RTL-Datapath Verification using Integer Linear Programming
ASP-DAC '02 Proceedings of the 2002 Asia and South Pacific Design Automation Conference
Principles of Constraint Programming
Principles of Constraint Programming
Embedding {0, ½}-Cuts in a Branch-and-Cut Framework: A Computational Study
INFORMS Journal on Computing
Operations Research Letters
Conflict analysis in mixed integer programming
Discrete Optimization
Operations Research Letters
Strengthening Chvátal-Gomory cuts and Gomory fractional cuts
Operations Research Letters
The Polytope of Context-Free Grammar Constraints
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Nonlinear Pseudo-Boolean Optimization: Relaxation or Propagation?
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
An Integrated Solver for Optimization Problems
Operations Research
Branch-cut-and-propagate for the maximum k-colorable subgraph problem with symmetry
CPAIOR'11 Proceedings of the 8th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Half reification and flattening
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
On the Facial Structure of the Alldifferent System
SIAM Journal on Discrete Mathematics
Rapid learning for binary programs
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Efficient and accurate haplotype inference by combining parsimony and pedigree information
ANB'10 Proceedings of the 4th international conference on Algebraic and Numeric Biology
Optimization in SMT with LA(Q) cost functions
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
Constraint satisfaction over bit-vectors
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
GloMIQO: Global mixed-integer quadratic optimizer
Journal of Global Optimization
Planning as satisfiability with IPC simple preferences and action costs
AI Communications
| Hi-index | 0.00 |
This article introduces constraint integer programming (CIP), which is a novel way to combine constraint programming (CP) and mixed integer programming (MIP) methodologies. CIP is a generalization of MIP that supports the notion of general constraints as in CP. This approach is supported by the CIP framework SCIP, which also integrates techniques from SAT solving. SCIP is available in source code and free for non-commercial use. We demonstrate the usefulness of CIP on two tasks. First, we apply the constraint integer programming approach to pure mixed integer programs. Computational experiments show that SCIP is almost competitive to current state-of-the-art commercial MIP solvers. Second, we employ the CIP framework to solve chip design verification problems, which involve some highly non-linear constraint types that are very hard to handle by pure MIP solvers. The CIP approach is very effective here: it can apply the full sophisticated MIP machinery to the linear part of the problem, while dealing with the non-linear constraints by employing constraint programming techniques.