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INOCS Research team
INtegrated Optimization with Complex Structure
- Leader : Luce Brotcorne
- Type : Project team
- Research center(s) : Lille
- Field : Applied Mathematics, Computation and Simulation
- Theme : Optimization, machine learning and statistical methods
- Partner(s) : Ecole Centrale de Lille,Université Libre de Bruxelles
- Collaborator(s) : E. CENTRALE LILLE, U. LILLE 1 (USTL), U. LILLE 3 (UCDG), CNRS, INRIA
Team presentation
An optimization problem consists in finding a best solution from a set of feasible solutions. Such a problem can be typically modeled as a mathematical program in which decision variables must (i) satisfy a set of constraints that translate the feasibility of the solution and (ii) optimize some (or several) objective function(s).
The INOCS team aims to develop new models, algorithmic techniques and implementations for optimization problems with complex structure.
More precisely, we consider that an optimization problem presents a complex structure when it involves decisions of different types/nature (i.e. strategic, tactical or operational), and/or presenting some hierarchical leader-follower structure (bilevel optimization) and/or taken in a uncertain environment (robust/stochastic optimization).
Research themes
- Sharply exploit the structure of the problem to define well suited models
- Develop integrated solution methods relying on mathematical programming (Exact methods or Math-Heuristics)
- Develop a tool for model structure detection
- Develop a toolbox:specific optimization methods for problems with complex structure
International and industrial relations
Alcatel
Colisweb
DHL
EDF R&D
Happychic
Keolis
Utocat
Keywords: Optimization Problem Mathematical Programming Robust Optimization Bilevel Programming
Research teams of the same theme :
- BONUS - Big Optimization aNd Ultra-Scale Computing
- CELESTE - mathematical statistics and learning
- GEOSTAT - Geometry and Statistics in acquisition data
- 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
Contact
Team leader
Luce Brotcorne
Tel.: +33 3 59 35 86 29
Secretariat
Tel.: +33 3 59 35 86 17