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Boosting Frank-Wolfe by Chasing Gradients

ICML 2020 · Optimization › Nonconvex and smooth optimization · approach: Stochastic Frank-Wolfe methods · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Chase the negative gradient using matching-pursuit steps rather than using only the vanilla Frank-Wolfe directionFrank-Wolfe · Sparse signal recoveryfunction value per iterationHelped
Chase the negative gradient using matching-pursuit steps rather than using only the vanilla Frank-Wolfe directionFrank-Wolfe · Traffic assignmentfunction value per oracle callHelped
Use line search instead of the short-step rule (ablation)BoostFW · Sparsity-constrained logistic regression (Gisette)function value per iterationHelped
Use the short-step rule instead of line search (ablation)BoostFW · Sparsity-constrained logistic regression (Gisette)CPU timeHelped
Use the short-step rule instead of line search (ablation)BoostFW · Collaborative filtering (MovieLens 100k)function value per iterationHelped
Use the line-search variant (ablation)BoostFW · Collaborative filtering (MovieLens 100k)CPU timeHelped
Use the short-step strategy instead of line search (ablation)BoostFW · Sparse signal recoveryfunction valueMixed
Set the pursuit-round cap to 1, reducing BoostFW to Frank-Wolfe (ablation)BoostFW · Frank-Wolfe optimizationprimal-gap convergenceHurt
Apply gradient pursuit to DICGDICG · Video co-localization (YouTube-Objects)duality gapHelped
Use DICG rather than BoostFWFrank-Wolfe-style methods · Video co-localization (YouTube-Objects)duality gapHelped
Use line search instead of the short-step rule (ablation)Away-Step Frank-Wolfe · Strongly convex optimization over polytopesconvergence rateNo clear effect

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