Matrix analysis
Solving quadratic (0,1)-problems by semidefinite programs and cutting planes
Mathematical Programming: Series A and B
Using constraint metaknowledge to reduce arc consistency computation
Artificial Intelligence
A 0-1 Quadratic Knapsack Problem for Modelizing and Solving the Constaint Satisfaction Problems
EPIA '97 Proceedings of the 8th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
AC-3d an Efficient Arc-Consistency Algorithm with a Low Space-Complexity
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Saving Support-Checks Does Not Always Save Time
Artificial Intelligence Review
Using a Mixed Integer Quadratic Programming Solver for the Unconstrained Quadratic 0-1 Problem
Mathematical Programming: Series A and B
Constraint satisfaction problem using modified branch and bound algorithm
WSEAS Transactions on Computers
Refining the basic constraint propagation algorithm
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Making AC-3 an optimal algorithm
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A framework for the definition and generation of artificial neural networks
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
Neural architectures optimization and genetic algorithms
WSEAS Transactions on Computers
The continuous hopfield networks (CHN) for the placement of the electronic circuits problem
WSEAS Transactions on Computers
An object-oriented software implementation of a novel cuckoo search algorithm
ECC'11 Proceedings of the 5th European conference on European computing conference
| Hi-index | 0.00 |
We consider the constraint satisfaction problem (CSP), where the values must be assigned to variables which are subject to a set of constraints. This problem is naturally formulated as 0-1 quadratic knapsack problem subject to quadratic constraint. In this paper, we present a branch-and-bound algorithm for 0-1 quadratic programming, which is based on solving semidefinite relaxations. At each node of the enumeration tree, a lower bound is given naturally by the value of (SDP) problem and an upper bound is computed by satisfying the quadratic constraint. We show that this method is able to determine whether a (CSP) has a solution or not. Then we give some hints on how to reduce as much as possible the initial size of the (CSP). Some numerical examples assess the effectiveness of the theoretical results shown in this paper, and the advantage of the new modelization.