Dec 7, 2021 04:00 PM Singapore (Registration will open at 03:50 PM.)
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Meeting ID: 924 5160 3689
In this presentation, I want to provide a high-level overview of a recent approach for analyzing and designing first-order methods using symbolic computations and/or semidefinite programming (SDP). This approach is commonly referred to as “performance estimation”. The presentation will be example-based, as the main ingredients necessary for understanding the methodologies are already present in the analysis of base optimization schemes. Relying on those examples, I want to provide an overview on a few principled approaches to the construction of (optimal) first-order methods for convex optimization. The methodology is implemented within two open source packages, allowing to use the framework without the SDP modelling steps.
Papers related to the talk:
Adrien is currently a researcher at INRIA and Ecole Normale Supérieure, in Paris, France. Before that, Adrien obtained a PhD from UCLouvain (2017, Belgium) where he worked under the supervision of François Glineur and Julien Hendrickx. His PhD was followed by a postdoc at INRIA/ENS, where he was supervised by Francis Bach before joining the same team as a researcher. Adrien’s PhD had the chance to be awarded a few local prizes (icteam, IBM-FNRS PhD thesis prizes), a 2017 OPTL best paper award, as well as to be a finalist of the 2015-2018 Tucker prize from the mathematical optimization society.
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