Randomized Block Frank–Wolfe for Convergent Large-Scale Learning
نویسندگان
چکیده
منابع مشابه
Randomized Block Frank-Wolfe for Convergent Large-Scale Learning
Owing to their low-complexity iterations, FrankWolfe (FW) solvers are well suited for various large-scale learning tasks. When block-separable constraints are present, randomized block FW (RB-FW) has been shown to further reduce complexity by updating only a fraction of coordinate blocks per iteration. To circumvent the limitations of existing methods, the present work develops step sizes for R...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2017
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2017.2755597