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Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization

2017
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PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Nowadays, asynchronous parallel algorithms have received much attention in the optimization field due to the crucial demands for modern large-scale optimization problems. However, most asynchronous algorithms focus on convex problems. Analysis on nonconvex problems is lacking. For the Asynchronous Stochastic Descent (ASGD) algorithm, the best result from (Lian et al., 2015) can only achieve an asymptotic O(\frac{1}{\epsilon^2}) rate (convergence to the stationary points) on nonconvex problems.

doi:10.1609/aaai.v31i1.10651
fatcat:o3kfopgbk5guhngxvlg3cbv7jm