Seminar Details
2020-05-19 (13:30) : Two 20-minute talks given by the students (1/2)
At MS Teams
Organized by Mathematical Engineering
Section 1: Random Projections, Compressive Sensing and Compressive Classification
Speaker :
Mattéo Couplet
Abstract :
About a decade ago, groundbreaking papers revealed the possibility of faithfully recovering high-dimensional signals from some incomplete information about them. They gave rise to the field of compressive sensing, which is now in a mature state and whose foundations rely on an elegant mathematical theory. This talk will start with an overview of the field. Then, the compressive classification task will be addressed, for which it can be shown that even fewer compressive measurements are required than for signal reconstruction, using ideas from high-dimensional convex geometry.
Section 2: Proximal methods
Speaker :
Calbert, Julien
Abstract :
In this talk, we will discuss a particular class of optimization algorithms: proximal algorithms. These algorithms are well suited to solve convex, non-smooth, constrained and large-scale optimization problems. They are based on the successive resolution of a convex optimization problem called proximal operator. These methods are of great interest because they work under general conditions, they can be fast if a closed form can be derived for the proximal operator and under certain conditions they can be solved in a distributed way. In addition, proximal methods can be seen as an additional level of abstraction to the classical optimization methods. They form a theoretical framework for demonstrating simultaneously the convergence of several optimization methods such as the gradient method or the projected gradient method. Finally, we will see several applications in the field of signal processing.