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 introduce and analyze stochastic optimization methods where the input to each update
is perturbed by bounded noise. We show that this framework forms the basis of a unified …

One of the beauties of the projected gradient descent method lies in its rather simple
mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its …

One of the most widely used optimization methods for large-scale machine learning
problems is distributed asynchronous stochastic gradient descent (DASGD). However, a key …

In this paper, we propose several improvements on the block-coordinate Frank-Wolfe
(BCFW) algorithm from Lacoste-Julien et al.(2013) recently used to optimize the structured …

Abstract The Conditional Gradient (or Frank-Wolfe) method is one of the most well-known
methods for solving constrained optimization problems appearing in various machine …

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 …

Market equilibrium is a solution concept with many applications such as digital ad markets,
fair division, and resource sharing. For many classes of utility functions, equilibria can be …

Abstract We consider the Frank-Wolfe (FW) method for constrained convex optimization, and
we show that this classical technique can be interpreted from a different perspective: FW …

Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe
method, have gained significant interest in the machine learning community in recent years …