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We 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 counter- part. 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.
Parallel Stochastic Newton Method M. Mutný, P. RichtárikIn Journal of Computational Mathematics, volume 36, 2018
Bibtex Entry:
	author = {Mutn{\'y}, Mojmir and Richt{\'a}rik, Peter},
	journal = {Journal of Computational Mathematics},
	number = {3},
	pages = {405-426},
	title = {Parallel Stochastic Newton Method},
	volume = {36},
	year = {2018}}