Geometric step decay yields local linear convergence for stochastic algorithms on sharp nonconvex problems and gives matching or new guarantees for phase retrieval and blind deconvolution under Gaussian and heavy-tailed measurements.
Proximal Stochastic Dual Coordinate Ascent
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abstract
We introduce a proximal version of dual coordinate ascent method. We demonstrate how the derived algorithmic framework can be used for numerous regularized loss minimization problems, including $\ell_1$ regularization and structured output SVM. The convergence rates we obtain match, and sometimes improve, state-of-the-art results.
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math.OC 1years
2019 1verdicts
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Stochastic algorithms with geometric step decay converge linearly on sharp functions
Geometric step decay yields local linear convergence for stochastic algorithms on sharp nonconvex problems and gives matching or new guarantees for phase retrieval and blind deconvolution under Gaussian and heavy-tailed measurements.