Derivative-Free Approaches for Chance-Constrained Problems with Right-Hand Side Uncertainty
New publication in SIAM Journal on Optimization: Derivative-Free Approaches for Chance-Constrained Problems with Right-Hand Side Uncertainty By Welington DE OLIVEIRA Abstract: This work addresses (mixed-integer) joint chance-constrained optimization problems in which the only uncertain parameter corresponds to the right-hand side coefficients in an inequality system. It exploits one-dimensional marginals [...]
Post-doctoral Researcher (M/W) – Position in Computational Stochastic Optimization
The CMA is hiring a Post-doctoral Researcher (M/W) for a position in Computational Stochastic Optimization Applications are invited for a one-year postdoctoral position at the Centre de Mathématiques Appliquées (CMA) of Mines Paris PSL. The position will be funded by PGMO (Programme Gaspard Monge pour l'Optimisation) as part of [...]
Participation of the CMA at COP29
Image credit: © Azerbaijan Government Participation of the CMA at COP29 : Side Event "Resources to achieve a just transition: levers and limits" The CMA Mines Paris - PSL will take part in the Side Event entitled "Resources to achieve a just transition: levers and limits" which will [...]
Computing Wasserstein barycenter via operator splitting: the method of averaged marginals
New publication in SIAM Journal on Mathematics of Data Science : Computing Wasserstein barycenter via operator splitting: the method of averaged marginals By Daniel W. MIMOUNI, Paul MALISANI, Jiamin ZHU and Welington DE OLIVEIRA Abstract : The Wasserstein barycenter (WB) is an important tool for summarizing sets of probability [...]
PhD defence of Sophie CHLELA | 7 November 2024
Sophie CHLELA will defend her thesis on 7 novembre 2024 at 2 pm in Amphithéâtre Mozart at Mines Paris – PSL, 1 rue Claude Daunesse, 06560 Sophia-Antipolis, France. Subject: Integrated modeling of the global energy system for pathways with carbon dioxide removal Thesis supervisor: Sandrine SELOSSE (CMA Mines Paris - [...]
Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study
New publication in Smart Energy: Optimal energy management in smart energy systems: A deep reinforcement learning approach and a digital twin case-study By Dhekra BOUSNINA et Gilles GUERASSIMOFF Abstract: This research work introduces a novel approach to energy management in Smart Energy Systems (SES) using Deep Reinforcement Learning [...]