Abstract The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks
in particular to its ability to nicely handle the structured constraints appearing in machine …

Convex optimization is at the core of many of today's analysis tools for large datasets, and in
particular machine learning methods. In this thesis we will study the general setting of …

We study the linear convergence of variants of the Frank-Wolfe algorithms for some classes
of strongly convex problems, using only affine-invariant quantities. As in Guelat & Marcotte …

It is well known that the gradient descent algorithm converges linearly when applied to a
strongly convex function with Lipschitz gradient. In this case, the algorithm's rate of …

Recently, there has been a renewed interest in the machine learning community for variants
of a sparse greedy approximation procedure for concave optimization known as the Frank …

Frank-Wolfe (FW) algorithms with linear convergence rates have recently achieved great
efficiency in many applications. Garber and Meshi (2016) designed a new decomposition …

We propose a generalized variant of Frank-Wolfe algorithm for solving a class of sparse/low-
rank optimization problems. Our formulation includes Elastic Net, regularized SVMs and …

The Frank-Wolfe algorithms, aka conditional gradient algorithms, solve constrained
optimization problems. They break down a non-linear problem into a series of linear …

Large-scale optimization problems abound in data mining and machine learning
applications, and the computational challenges they pose are often addressed through …

Frank-Wolfe algorithms for convex minimization have recently gained considerable attention
from the Optimization and Machine Learning communities, as their properties make them a …