Clément W. Royer
La version française de cette page se trouve ici.
Welcome to my web page. Here you can learn a little about me and my research interests. You can also have a look at my publications, my teaching activities, the codes that I have written and the talks that I have given.
Enjoy your visit !
Two journal papers accepted in the beginning of 2019!
In Direct search based on probabilistic feasible descent for bound and linearly constrained problems
, we proposed a randomized derivative-free algorithm with probabilistic guarantees. The method is particularly appropriate for exploring subspaces in which the constraints are not active. The code
used in the paper (in MATLAB) is available for download.
In A Newton-CG algorithm with complexity guarantees for smooth unconstrained optimization
, we equipped the classical Newton+Conjugate Gradient paradigm with a complexity analysis, and made interesting connections with accelerated gradient methods. An experimental study is underway; meanwhile, you can find some of the results in my latest presentation
on this topic (given in January at my alma mater, University of Toulouse, France).
I am a postdoctoral research associate within the Wisconsin Institute for Discovery, in Madison, Wisconsin (USA). I am fortunate to be supervised by Stephen J. Wright.
On November 4, 2016, I was granted a PhD in applied mathematics from the university of Toulouse, delivered by the Université Toulouse III Paul Sabatier. It was prepared at the Institut de Recherche en Informatique de Toulouse, under the joint supervision of Serge Gratton and Luís Nunes Vicente.
From 2013 to 2016, I was a teaching assistant (moniteur, in charge of lab and tutorial sessions) at the French Engineering School ENSEEIHT.
I obtained my Engineer degree (equivalent to Master's Degree) in Computer Science and Applied Mathematics, as well as my Master in Computer Science from Toulouse INP (National Polytechnic Institute).
For more information, you can have a look at my vitae in a short or extended format.
My research essentially revolves around the field of numerical optimization and its applications, particularly in complex systems and data science.
My current work aims at developing efficient nonconvex optimization algorithms, with
a focus on incorporating randomness (typically within linear algebra techniques), and establishing complexity guarantees for those frameworks.
Following the lines of my Ph.D., I also maintain a high interest in derivative-free optimization and its applications to solving simulation-based problems.
This page was designed by Clothilde Royer, many thanks to her.
Materials on this page are available under Creative Commons CC BY-NC 4.0