We investigate the resolution of second-order, potential, and monotone mean field games
with the generalized conditional gradient algorithm, an extension of the Frank-Wolfe …

We propose a fully-corrective generalized conditional gradient method (FC-GCG) for the
minimization of the sum of a smooth, convex loss function and a convex one-homogeneous …

This article investigates the convergence of the Generalized Frank–Wolfe (GFW) algorithm
for the resolution of potential and convex second-order mean field games. More specifically …

Clustering complex high-dimensional data is particularly challenging as the signal-to-noise
ratio in such data is significantly lower than their classical counterparts. This is mainly …

We show that the conditional gradient method for the convex composite problem generates
primal and dual iterates with a duality gap converging to zero provided a suitable growth …

Motivated by optimization with differential equations, we consider optimization problems with
Hilbert spaces as decision spaces. As a consequence of their infinite dimensionality, the …

Nonsmooth composite optimization problems under uncertainty are prevalent in various
scientific and engineering applications. We consider risk-neutral composite optimal control …

This paper introduces a preconditioned convex splitting algorithm enhanced with line search
techniques for nonconvex optimization problems. The algorithm utilizes second-order …

An extension of the Frank-Wolfe Algorithm (FWA), also known as Conditional Gradient
algorithm, is proposed. In its standard form, the FWA allows to solve constrained …

This thesis deals with the numerical analysis and methods for optimization problems and
potential games involving a large number of agents. We consider asymptotic models …