PEREZ , Guillaume

Multivalued Decision Diagrams (MDDs) are efficient data structures widely used in several fields like verification, optimization and dynamic programming. In this thesis, we first focus on improving the main algorithms such as the reduction, allowing MDDs to potentially exponentially compress set of tuples, or the combination of MDDs such as the...

In constraint programming the search strategy entirely guides the solving process, and drastically affects the running time for solving particular problem instances. Many features have been defined so far for the design of efficient and robust search strategies, such as variables' domains, constraint graph, or even the constraints triggering...

This papers extends in three ways our previous work about efficient operations on Multi-valued Decision Diagrams (MDD) for building Constraint Programming models. First, we improve the existing methods for transforming a set of tuples, Global Cut Seeds or sequences of tuples into MDDs. Then, we present in-place algorithms for adding and...

We propose improved algorithms for defining the most common operations on Multi-Valued Decision Diagrams (MDDs): creation, reduction, complement , intersection, union, difference, symmetric difference, complement of union and complement of intersection. Then, we show that with these algorithms and thanks to the recent development of an...

Multi-valued Decision Diagrams (MDDs) have been extensively studied in the last ten years. Recently, efficient algorithms implementing operators such as reduction, union, intersection , difference, etc., have been designed. They directly deal with the graph structure of the MDD and a time reduction of several orders of magnitude in comparison...

We introduce GAC-4R, MDD-4, and MDD-4R three new algorithms for maintaining arc consistency for table and MDD constraints. GAC-4R improves the well-known GAC-4 algorithm by managing the internal data structures in a different way. Instead of maintaining the internal data structures only by studying the consequences of deletions, we propose to...

Projected gradient descent has been proved efficient in many optimization and machine learning problems. The weighted 1 ball has been shown effective in sparse system identification and features selection. In this paper we propose three new efficient algorithms for projecting any vector of finite length onto the weighted 1 ball. The first two...

National audience

We propose in this paper a new method processing the projection of an arbitrary size vector onto the probabilistic simplex or the 1 ball. Our method merges two principles. The first one is an original search of the projection using a bucket algorithm. The second one is a filtering, on the fly, of the values that cannot be part of the...