The graph isomorphism problem consists in deciding if two given graphs have an identical structure. This problem can be modeled as a constraint satisfaction problem in a very straightforward way, so that one can use constraint programming to solve it. However, constraint programming is a generic tool that may be less efficient than dedicated algorithms which can take advantage of the global sem...

In finding all solutions to a constraint satisfaction problem, or proving that there are none, with a search algorithm that backtracks chronologically and forms k-way branches, the order in which the values are assigned is immaterial. However, we show that if the values of a variable are assigned instead via a sequence of binary choice points, and the removal of the value just tried from the do...

The aim of this work is to investigate the variational discretization and mixed finite element methods for optimal control problem governed by semi linear parabolic equations with integral constraint. The state and co-state are approximated by the lowest order Raviart-Thomas mixed finite element spaces and the control is not discreted. Optimal error estimates in L2 are established for the state...

We determine under which conditions certain natural models of random constraint satisfaction problems have sharp thresholds of satisfiability. These models include graph and hypergraph homomorphism, the (d, k, t)-model, and binary constraint satisfaction problems with domain size 3.

To model combinatorial decision problems involving uncertainty and probability, we have proposed “stochastic constraint programming” [3]. This extends constraint programming with stochastic variables, chance constraints and optimized expectations. We propose extending the OPL modelling language [1] with these features, and show how they can be compiled away using some simple rules.