Bounding the Optimum of Constraint Optimization Problems

@inproceedings{Givry1997BoundingTO,
  title={Bounding the Optimum of Constraint Optimization Problems},
  author={Simon de Givry and G{\'e}rard Verfaillie and T. Schiex},
  booktitle={CP},
  year={1997}
}
Solving constraint optimization problems is computationally so expensive that it is often impossible to provide a guaranteed optimal solution, either when the problem is too large, or when time is bounded. In these cases, local search algorithms usually provide good solutions. However, and even if an optimality proof is unreachable, it is often desirable to have some guarantee on the quality of the solution found, in order to decide if it is worthwile to spend more time on the problem. This… 

Anytime Lower Bounds for Constraint Violation Minimization Problems

It is shown that a new algorithm, resulting from a combination of the Russian Doll Search and Iterative Deepening algorithms, clearly outperforms five known algorithms and allows high lower bounds to be rapidly produced.

Optimization Methods for Constraint Resource Problems

A tight lower bound of the problem optimum is obtained by adding redundant constraints that take into account the "wastage" in a partial solution by using resource optimization methods for solving efficiently synthesis problems in a constraint-based framework.

Multi-Objective Constraint Optimization with Lexicographic Preference Models

A branch-and-bound algorithm to find optimal solutions of Multi-Objective Constraint Optimization Problems (MOCOP), which uses a minibuckets algorithm for generating the upper bound at each node, as proposed in [2].

Bucket elimination for multiobjective optimization problems

This paper extends bucket elimination (BE), a well known dynamic programming generic algorithm, from mono-objective to multiobjective optimization and shows that the resulting algorithm, MO-BE, can be applied to true multi-Objective problems as well as mono- objective problems with knapsack (or related) global constraints.

De ned Constraint Satisfaction Problems with Nearest Neighbour Method

This paper enhances traditional CSP techniques with k-nearest neighbour methods (kNN) and uses past solutions as implicit knowledge of the unknown parts of the problems, while it employs a tree search to solve CSPs.

Up and Down Mini-Buckets: A Scheme for Approximating Combinatorial Optimization Tasks

UD-MB is presented, a new algorithm that applies the mini-bucket elimination idea to accomplish the problem of computing lower bounds on the optimal costs associated with each unary assignment of a value to a variable in combinatorial optimization problems.

Abstracting soft constraints: Framework, properties, examples

Contributions to search and inference algorithms for csp and weighted csp

This thesis presents a collection of new algorithms for solving Constraint Satisfaction Problem (CSP) and Weighted Constraints Satisfaction problem (WCSP), and presents some new inference operations that permit us to factorize a constraint into a set of smaller size constraints.

Up and Down Mini-Buckets : A Scheme for Approximating Combinatorial Optimization Tasks Tracking number : 708

UD-MB is presented, a new algorithm that applies the mini-bucket elimination idea to accomplish the problem of computing lower bounds on the optimal costs associated with each unary assignment of a value to a variable in combinatorial optimization problems.

Arc consistency for soft constraints

References

SHOWING 1-10 OF 25 REFERENCES

Russian Doll Search for Solving Constraint Optimization Problems

The Russian Doll Search algorithm is introduced, which replaces one search by n successive searches on nested subproblems, records the results of each search and uses them later, when solving larger subpro problems, in order to improve the lower bound on the global valuation of any partial assignment.

Analysis of Heuristic Methods for Partial Constraint Satisfaction Problems

An important finding is that a version of min-conflicts that incorporates the random walk strategy, with a good value for the walk probability appears to be as efficient in this domain as several of the more elaborate methods for improving local search that have been proposed in recent years.

An Optimal Admissible Tree Search

This heuristic depth-first iteratiw-deepening algorithm is the only known algorithm that is capable of finding optimal solutions to randomly generated instances of the Fifeen Puzzle within practical resource limits.

Directed Arc Consistency Preprocessing

  • R. Wallace
  • Computer Science
    Constraint Processing, Selected Papers
  • 1995
This work describes a family of strategies based on directed arc consistency testing during preprocessing that retain the benefits of full arc consistency checking, while improving lower bound calculations.

Partial Constraint Satisfaction

Local Search in Combinatorial Optimisation.

This book is an important reference volume and an invaluable source of inspiration for advanced students and researchers in discrete mathematics, computer science, operations research, industrial engineering and management science.

Nogood Recording for static and dynamic constraint satisfaction problems

A new class of constraint recording algorithms called Nogood Recording is proposed that may be used for solving both static and dynamic CSPs and offers an interesting compromise, polynomially bounded in space, between an ATMS-like approach and the usual static constraint satisfaction algorithms.

Valued Constraint Satisfaction Problems: Hard and Easy Problems

A simple algebraic framework is considered, related to Partial Constraint Satisfaction, which subsumes most of these proposals and is used to characterize existing proposals in terms of rationality and computational complexity.

MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?) on Hard Problems

This paper tries to convince once and for all the CSP community that MAC is not only more efficient than FC to solve large practical problems, but it is also really more efficient on hard and large random problems.

Nogood recording for valued constraint satisfaction problems

  • Pierre DagoG. Verfaillie
  • Computer Science
    Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence
  • 1996
This study aims to use Nogood Recording in the wider scope of the Valued CSP framework (VCSP) to enhance the branch and bound algorithm and uses nogoods to increase the lower bound used by the branches and bound to prune the search.