BONUS Research team
Big Optimization aNd Ultra-Scale Computing
- Leader : Nouredine Melab
- Type : Project team
- Research center(s) : Lille
- Field : Applied Mathematics, Computation and Simulation
- Theme : Optimization, machine learning and statistical methods
- Partner(s) : Université des sciences et technologies de Lille (Lille 1)
- Collaborator(s) : E. CENTRALE LILLE, U. LILLE 1 (USTL), U. LILLE 3 (UCDG), CNRS, INRIA
Being ubiquitous to countless modern engineering and scientic applications, big optimization requires models increasingly large-scale to deal with a growing amount of decision variables and conflicting expensive objectives. The goal of Bonus is to come up with cutting-edge approaches at the interface of three research lines that constitute the scientic program of the project and the subject of current and forthcoming collaborations: decomposition-based optimization, Machine Learning-assisted optimization and ultra-scale optimization. From application and industrial transfer point of view, we target sustainability-aware complex scheduling and engineering design applications.
Big optimization problems (BOPs) refer to problems composed of a large number of environmental input parameters and/or decision variables (high dimensionality), and/or many objective functions that may be computationally expensive. Solving BOPs raises at least four major challenges: (1) tackling their high dimensionality ; (2) handling many objectives ; (3) dealing with computationally expensive objective functions ; and (4) scaling on (ultra-scale) modern supercomputers. The overall scientific objective of the Bonus project is to address efficiently these challenges using the three following research lines:
- Decomposition-based optimization. Given the large scale of the targeted optimization problems of Bonus in terms of the number of variables and objectives, their decomposition into smaller, easier to solve and loosely coupled or independent subproblems is essential to raise the challenge of scalability.
- Machine Learning-assisted Optimization. The objective of ML-aided optimization is to raise the challenge of expensive functions of Big Optimization problems (BOPs) using surrogates but also to assist the two other research lines in dealing with the other challenges of Bonus (high dimensionality and scalability).
- Ultra-scale optimization. This research line intensifies our difference from other (project-)teams of the related Inria scientific theme. It is complementary to the two other ones, which are sources of massive parallelism and with which it is combined to solve BOPs. Indeed, ultra-scale computing is necessary for the effective resolution of the large amount of subproblems generated by decomposition of BOPs, the parallel evaluation of simulation-based fitnesses and metamodels, etc.
International and industrial relations
- EDF, GDF-Suez.
- Beckman & Coulter
- ONERA et CNES
- Univ. Luxembourg, Université de Mons, Shinshu University (JAPAN), City University (Hong Kong), Georgia Tech (USA), University of Coimbra and University of Lisbon (PORTUGAL), etc.
Research teams of the same theme :
- CELESTE - mathematical statistics and learning
- GEOSTAT - Geometry and Statistics in acquisition data
- INOCS - INtegrated Optimization with Complex Structure
- MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
- MODAL - MOdel for Data Analysis and Learning
- RANDOPT - Randomized Optimization
- REALOPT - Reformulations based algorithms for Combinatorial Optimization
- SEQUEL - Sequential Learning
- SIERRA - Statistical Machine Learning and Parsimony
- TAU - Tackling the under-specified