Tangi Migot

Tangi Migot

Computational Mathematician

Stopping v.0.1

less than 1 minute read

Published:

I gave a talk entitled “Stopping.jl: A framework to implement iterative optimization algorithms” in the Journées de l’optimisation 2019, Montréal, Québec in the session Optimization in Julia to introduce our new Julia package.

You can find the slides here and the package is available here (for now):

Stopping.jl

This package is a set of tools to ease the uniformization of stopping criteria in iterative solvers.

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ARCqK published in Mathematical Programming

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I am thrilled to share that the article Scalable adaptive cubic regularization methods has been published in the journal Mathematical Programming, Series A. This has been a really exciting journey with my co-authors Jean-Pierre Dussault and Dominique Orban on this really exciting work that I hope will help explore the numerical possibilities of ARC methods. The proposed implementation is a perfect fit for large-scale application as it solves the subproblem inexactly and only required Hessian-vector products, so no need to evaluate and store the Hessian matrix. As usual, the code has been done in Julia and is available in the folder paper in the Github repository AdaptiveRegularization.jl. Full text published version available from here, enjoy!

Looking Back at the Winter Session

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In the world of numerical optimization, the quest for efficiency, accuracy, and innovation never ceases. At Polytechnique Montréal, students embarking on their educational journey have a unique opportunity to explore this dynamic field through the course MTH8408 Méthodes d’optimisation et contrôle optimal. This course, which I had the privilege of teaching during the winter session of 2023, delves into the depths of numerical methods for optimization, variational calculus, and optimal control.

Unveiling JuliaSmoothOptimizers at JuMP-dev Workshop, JuliaCon 2023

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I am thrilled to share my experiences from the recent JuMP-dev workshop that took place at the JuliaCon 2023, held at MIT in Boston, USA. As a passionate researcher in the field of numerical optimization, this year’s conference was particularly special for me, as it marked my first in-person attendance after the previous year’s online edition (read about it here). You can check the replay of my talk on youtube.

PDENLPModels.jl published in JOSS

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I am very happy to announce the publication in the Journal of Open Source Software of the paper PDENLPModels.jl: A NLPModel API for optimization problems with PDE-constraints.