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- W4287604311 abstract "It is known that the Frank-Wolfe (FW) algorithm, which is affine-covariant, enjoys accelerated convergence rates when the constraint set is strongly convex. However, these results rely on norm-dependent assumptions, usually incurring non-affine invariant bounds, in contradiction with FW's affine-covariant property. In this work, we introduce new structural assumptions on the problem (such as the directional smoothness) and derive an affine invariant, norm-independent analysis of Frank-Wolfe. Based on our analysis, we propose an affine invariant backtracking line-search. Interestingly, we show that typical backtracking line-searches using smoothness of the objective function surprisingly converge to an affine invariant step size, despite using affine-dependent norms in the step size's computation. This indicates that we do not necessarily need to know the set's structure in advance to enjoy the affine-invariant accelerated rate." @default.
- W4287604311 created "2022-07-25" @default.
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- W4287604311 date "2020-11-06" @default.
- W4287604311 modified "2023-10-14" @default.
- W4287604311 title "Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets" @default.
- W4287604311 doi "https://doi.org/10.48550/arxiv.2011.03351" @default.
- W4287604311 hasPublicationYear "2020" @default.
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