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… (More)
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Topic mentions per year

Topic mentions per year

1971-2017
020406019712017

Papers overview

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Review
2017
Review
2017
We extend the Frank-Wolfe (FW) optimization algorithm to solve constrained smooth convex-concave saddle point (SP) problems… (More)
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2016
2016
We show that k-means clustering is a matrix factorization problem. Seen from this point of view, k-means clustering can be… (More)
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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… (More)
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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… (More)
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2015
2015
It is known that the gradient descent algorithm converges linearly when applied to a strongly convex function with Lipschitz… (More)
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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… (More)
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Highly Cited
2013
Highly Cited
2013
We provide stronger and more general primal-dual convergence results for FrankWolfe-type algorithms (a.k.a. conditional gradient… (More)
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Highly Cited
2013
Highly Cited
2013
We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block… (More)
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2010
2010
The high computational cost of nonlinear support vector machines has limited their usability for large-scale problems. We propose… (More)
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Review
2008
Review
2008
The problem of maximizing a concave function <i>f(x)</i> in the unit simplex &#916; can be solved approximately by a simple… (More)
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