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 present and analyze an away-step Frank–Wolfe method for the convex optimization
problem min x∈ X f (A x)+〈 c, x〉, where f is a θ-logarithmically homogeneous self …

With the well-documented popularity of Frank Wolfe (FW) algorithms in machine learning
tasks, the present paper establishes links between FW subproblems and the notion of …

Invented some 65 years ago in a seminal paper by Marguerite Straus-Frank and Philip
Wolfe, the Frank–Wolfe method recently enjoys a remarkable revival, fuelled by the need of …

Cluster detection plays a fundamental role in the analysis of data. In this paper, we focus on
the use of s-defective clique models for network-based cluster detection and propose a …

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 …

Approximate Bayesian Computation (ABC) enables statistical inference in simulatorbased
models whose likelihoods are difficult to calculate but easy to simulate from. ABC constructs …

Frank-Wolfe algorithms (FW) are popular first-order methods for solving constrained convex
optimization problems that rely on a linear minimization oracle instead of potentially …

Projection-free conditional gradient (CG) methods are the algorithms of choice for
constrained optimization setups in which projections are often computationally prohibitive …

We provide a template to derive convergence rates for the following popular versions of the
Frank-Wolfe algorithm on polytopes: vanilla Frank-Wolfe, Frank-Wolfe with away steps …