Journal ArticleDOI
Some comments of Wolfe's `away step'
J Guélat,Patrice Marcotte +1 more
TLDR
It is given a detailed proof, under slightly weaker conditions on the objective function, that a modified Frank-Wolfe algorithm based on Wolfe's ‘away step’ strategy can achieve geometric convergence, provided a strict complementarity assumption holds.Abstract:
We give a detailed proof, under slightly weaker conditions on the objective function, that a modified Frank-Wolfe algorithm based on Wolfe's ‘away step’ strategy can achieve geometric convergence, provided a strict complementarity assumption holds.read more
Citations
More filters
Posted Content
Boosting Frank-Wolfe by Chasing Gradients
TL;DR: This paper proposes to speed up the Frank-Wolfe algorithm by better aligning the descent direction with that of the negative gradient via a subroutine, and derives convergence rates to $\mathcal{O}(1/t)$ to $e^{-\omega t})$ of the method.
Proceedings ArticleDOI
A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification
TL;DR: This paper investigates the application of Frank-Wolfe algorithms to Machine Learning, focusing in particular on a Parallel Tangent (PARTAN) variant of the FW algorithm for SVM classification, which has not been previously suggested or studied for this type of problem.
Journal ArticleDOI
Revisiting the approximate Carathéodory problem via the Frank-Wolfe algorithm
TL;DR: In this article, the authors revisited the approximate Caratheodory problem by solving the primal problem via the Frank-Wolfe algorithm, providing a simplified analysis and leading to an efficient practical method.
Posted Content
Avoiding bad steps in Frank Wolfe variants
TL;DR: The Short Step Chain (SSC) procedure, which skips gradient computations in consecutive short steps until proper stopping conditions are satisfied, is defined, which allows a unified analysis and converge rates in the general smooth non convex setting, as well as a linear convergence rate under a Kurdyka-Lojasiewicz (KL).
Posted Content
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee
TL;DR: In this paper, the authors present a high-performance implementation of the Frank-Wolfe (FW) method tailored to solve large-scale Lasso regression problems, based on a randomized iteration, and prove that the convergence guarantees of the standard FW method are preserved in the stochastic setting.
References
More filters