Portrait of Antoine Lesage-Landry

Antoine Lesage-Landry

Associate Academic Member
Assistant Professor, Polytechnique Montréal, Department of Electrical Engineering

Biography

I am an assistant professor in the Department of Electrical Engineering at Polytechnique Montréal. I received my BEng degree in engineering physics from Polytechnique Montréal in 2015, and my PhD degree in electrical engineering from the University of Toronto in 2019. I was a postdoctoral scholar in the Energy & Resources Group at the University of California, Berkeley, from 2019 to 2020. My research interests include optimization, online learning and machine learning, and their application to power systems with renewable generation.

Current Students

Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Co-supervisor :
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Co-supervisor :
Master's Research - Polytechnique Montréal
PhD - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Master's Research - Polytechnique Montréal
Research Intern - Polytechnique Montréal Paris
Master's Research - Polytechnique Montréal

Publications

An Online Newton’s Method for Time-Varying Linear Equality Constraints
Jean-Luc Lupien
We consider online optimization problems with time-varying linear equality constraints. In this framework, an agent makes sequential decisio… (see more)ns using only prior information. At every round, the agent suffers an environment-determined loss and must satisfy time-varying constraints. Both the loss functions and the constraints can be chosen adversarially. We propose the Online Projected Equality-constrained Newton Method (OPEN-M) to tackle this family of problems. We obtain sublinear dynamic regret and constraint violation bounds for OPEN-M under mild conditions. Namely, smoothness of the loss function and boundedness of the inverse Hessian at the optimum are required, but not convexity. Finally, we show OPEN-M outperforms state-of-the-art online constrained optimization algorithms in a numerical network flow application.