The purpose of this survey is to serve both as a gentle introduction and a coherent overview
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …

We study the problem of active pure exploration with fixed confidence in generic stochastic
bandit environments. The goal of the learner is to answer a query about the environment …

There is growing interest in designing recommender systems that aim at being fair towards
item producers or their least satisfied users. Inspired by the domain of inequality …

We consider the classical setting of optimizing a nonsmooth Lipschitz continuous convex
function over a convex constraint set, when having access to a (stochastic) first-order oracle …

Dimensionality reduction is a classical technique widely used for data analysis. One
foundational instantiation is Principal Component Analysis (PCA), which minimizes the …

A fundamental model of quantum computation is the programmable quantum gate array.
This is a quantum processor that is fed by a program state that induces a corresponding …

This paper studies first-order algorithms for solving fully composite optimization problems
over convex and compact sets. We leverage the structure of the objective by handling its …

In this paper we provide an introduction to the Frank-Wolfe algorithm, a method for smooth
convex optimization in the presence of (relatively) complicated constraints. We will present …

We propose an algorithm which appears to be the first bridge between the fields of
conditional gradient methods and abs-smooth optimization. Our problem setting is motivated …

Frank and Wolfe's celebrated conditional gradient method is a well-known tool for solving
smooth optimization problems for which minimizing a linear function over the feasible set is …