KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/670054
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AbstractWe propose a parallel stochastic Newton method (PSN) for minimizing unconstrained smooth convex functions. We analyze the method in the strongly convex case, and give conditions under which acceleration can be expected when compared to its serial counterpart. We show how PSN can be applied to the large quadratic function minimization in general, and empirical risk minimization problems. We demonstrate the practical efficiency of the method through numerical experiments and models of simple matrix classes.
CitationRichtárik, M. M. and P. (2018). Parallel Stochastic Newton Method. Journal of Computational Mathematics, 36(3), 404–425. doi:10.4208/jcm.1708-m2017-0113
PublisherGlobal Science Press