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20112023
most citedAn Application of Deep Reinforcement Learning to Algorithmic Trading

222 citations · 600 across the 46 of their papers we have counts for

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6 papers · 1 filter

math.OC2023

Multi-objective near-optimal necessary conditions for multi-sectoral planning

Antoine Dubois, Jonathan Dumas, Paolo Thiran +2

This paper extends the concepts of epsilon-optimal spaces and necessary conditions for near-optimality from single-objective to multi-objective optimisation. These notions are firs…

math.OC2021

Computing Necessary Conditions for Near-Optimality in Capacity Expansion Planning Problems

Antoine Dubois, Damien Ernst

In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supra-national level. However, their cost-optimal solutions are often…

math.OC2017★ 53 cited

An Optimal Control Formulation of Pulse-Based Control Using Koopman Operator

Aivar Sootla, Alexandre Mauroy, Damien Ernst

In many applications, and in systems/synthetic biology, in particular, it is desirable to compute control policies that force the trajectory of a bistable system from one equilibri…

math.OC2014

Relaxations for multi-period optimal power flow problems with discrete decision variables

Quentin Gemine, Damien Ernst, Quentin Louveaux +1

We consider a class of optimal power flow (OPF) applications where some loads offer a modulation service in exchange for an activation fee. These applications can be modeled as mul…

math.OC2011★ 28 cited

Scenario trees and policy selection for multistage stochastic programming using machine learning

Boris Defourny, Damien Ernst, Louis Wehenkel

We propose a hybrid algorithmic strategy for complex stochastic optimization problems, which combines the use of scenario trees from multistage stochastic programming with machine…

math.OC2011

Optimal discovery with probabilistic expert advice

Sébastien Bubeck, Damien Ernst, Aurélien Garivier

We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it…