Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
Instances of constraint satisfaction problems can be solved efficiently if they are representable as a tree decomposition of small width. Unfortunately, the task of finding a deco...
Generating test inputs for a path in a function with integer and real parameters is an important but difficult problem. The problem becomes more difficult when pointers are pass...
Lagrangian relaxation is commonly used in combinatorial optimization to generate lower bounds for a minimization problem. We propose a modified Lagrangian relaxation which used i...
Abstract. Constraint Programming (CP) offers a rich modeling language of constraints embedding efficient algorithms to handle complex and heterogeneous combinatorial problems. To s...
Sophie Demassey, Gilles Pesant, Louis-Martin Rouss...