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Frank–Wolfe algorithm

Known as: Conditional gradient method, Frank-Wolfe, Frank-Wolfe algorithm 
The Frank–Wolfe algorithm is an iterative first-order optimization algorithm for constrained convex optimization. Also known as the conditional… Expand
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Papers overview

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Highly Cited
2016
Highly Cited
2016
We give a simple proof that the Frank-Wolfe algorithm obtains a stationary point at a rate of $O(1/\sqrt{t})$ on non-convex… Expand
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Highly Cited
2016
Highly Cited
2016
We present new results for the Frank–Wolfe method (also known as the conditional gradient method). We derive computational… Expand
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2016
2016
We study parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter… Expand
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Highly Cited
2015
Highly Cited
2015
The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks in particular to its ability to nicely handle… Expand
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Highly Cited
2015
Highly Cited
2015
The Frank-Wolfe method (a.k.a. conditional gradient algorithm) for smooth optimization has regained much interest in recent years… Expand
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Highly Cited
2015
Highly Cited
2015
There is renewed interest in formulating integration as a statistical inference problem, motivated by obtaining a full… Expand
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2015
2015
Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem… Expand
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Highly Cited
2014
Highly Cited
2014
In this paper, we tackle the problem of performing efficient co-localization in images and videos. Co-localization is the problem… Expand
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Highly Cited
2013
Highly Cited
2013
We provide stronger and more general primal-dual convergence results for Frank-Wolfe-type algorithms (a.k.a. conditional gradient… Expand
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Highly Cited
2013
Highly Cited
2013
We study the linear convergence of variants of the Frank-Wolfe algorithms for some classes of strongly convex problems, using… Expand
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