This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

The {\L} ojasiewicz inequality shows that H\" olderian error bounds on the minimum of
convex optimization problems hold almost generically. Here, we clarify results of\citet …

Collision detection between two convex shapes is an essential feature of any physics
engine or robot motion planner. It has often been tackled as a computational geometry …

Abstract The Pairwise Conditional Gradients (PCG) algorithm is a powerful extension of the
Frank-Wolfe algorithm leading to particularly sparse solutions, which makes PCG very …

Abstract The Frank-Wolfe method solves smooth constrained convex optimization problems
at a generic sublinear rate of $\mathcal {O}(1/T) $, and it (or its variants) enjoys accelerated …

We present a blended conditional gradient approach for minimizing a smooth convex
function over a polytope P, combining the Frank {–} Wolfe algorithm (also called conditional …

First-order methods for solving convex optimization problems have been at the forefront of
mathematical optimization in the last 20 years. The rapid development of this important class …

Collision detection is a fundamental problem in various domains, such as robotics,
computational physics, and computer graphics. In general, collision detection is tackled as a …

Generalized self-concordance is a key property present in the objective function of many
important learning problems. We establish the convergence rate of a simple Frank-Wolfe …

In this paper, we study active set identification results for the away-step Frank--Wolfe
algorithm in different settings. We first prove a local identification property that we apply, in …