Big Optimization aNd Ultra Scale computing

The "Big Optimization aNd Ultra Scale computing" (BONUS) research group is a joint project-team between Inria and the University of Lille withing the CRIStAL research center (UMR 9189, Univ Lille, CNRS, Inria, EC Lille), France.

Bonus was created as a team in July 2017, and became a project-team in June 2019. It is located at EuraTechnologies (Inria "Place" building).

Big optimization problems (BOPs) refer to complex 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 (curse of) 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 team is to address efficiently these challenges using the three following research lines.

Research axis #1 — 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.

Research axis #2 — 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).

Research axis #3 — 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.