In this paper, we show that the Away-step Stochastic Frank-Wolfe (ASFW) and Pairwise Stochastic Frank-Wolfe (PSFW) algorithms converge linearly in ...
Mar 21, 2017 · In order to prove these results, we develop a novel proof technique based on concepts of empirical processes and concentration inequalities.
This technique is then applied to prove the linear convergence in expectation and almost sure convergence of each sample path of another Frank-Wolfe variant, ...
Apr 14, 2017 · In order to prove these results, we develop a novel proof technique based on concepts of empirical processes and concentration inequalities.
In this paper, we show that the Away-step Stochastic Frank-Wolfe Algorithm (ASFW) and Pairwise Stochastic Frank-Wolfe algorithm (PSFW) converge linearly in ...
We clarify several variants of the Frank-Wolfe algorithm and show that they all converge linearly for any strongly convex function optimized over a polytope ...
In this last chapter we will describe and study the convergence of the stochastic variants of two kinds of Block-Coordinate Frank-Wolfe algorithms with SSC seen ...
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Jul 14, 2021 · This paper considers stochastic convex optimization problems with two sets of constraints: (a) deterministic constraints on the domain of the optimization ...
Dec 13, 2023 · In this paper we provide an introduction to the Frank-Wolfe algorithm, a method for smooth convex optimization in the presence of (relatively) complicated ...
The proposed rank-$k$ variant of the classical Frank-Wolfe algorithm to solve convex optimization over a trace-norm ball has a linear convergence rate when ...