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
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Journal ArticleDOI
Active Set Complexity of the Away-Step Frank--Wolfe Algorithm
TL;DR: In this paper, active set identification results for the away-step Frank-Wolfe algorithm in different settings were studied. But the results were only applied to combinatorial settings.
Proceedings ArticleDOI
Background Subtraction via Fast Robust Matrix Completion
Behnaz Rezaei,Sarah Ostadabbas +1 more
TL;DR: The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.
Posted Content
Blended Conditional Gradients: the unconditioning of conditional gradients.
TL;DR: The authors presented a blended conditional gradient approach for minimizing a smooth convex function over a polytope P, combining the Frank-Wolfe algorithm (also called conditional gradient) with gradient-based steps, different from away steps and pairwise steps.
Proceedings Article
{Similarity Learning for High-Dimensional Sparse Data}
Kuan Liu,Aurélien Bellet,Fei Sha +2 more
TL;DR: In this article, the similarity measure is parameterized as a convex combination of rank-one matrices with specic sparsity structures, and the parameters are then optimized with an approximate Frank-Wolfe procedure to maximally satisfy relative similarity constraints on the training data.
Proceedings Article
Linear Convergence of Stochastic Frank Wolfe Variants
TL;DR: In this paper, it was shown that the Away-step stochastic Frank-Wolfe Algorithm (ASFW) and Pairwise Stochastic Frank Wolfe algorithm (PSFW) converge linearly in expectation.
References
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