RES: Regularized Stochastic BFGS Algorithm

RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method, is proposed to solve strongly convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to a...

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Vydané v:	IEEE transactions on signal processing Ročník 62; číslo 23; s. 6089 - 6104
Hlavní autori:	Mokhtari, Aryan, Ribeiro, Alejandro
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation Approximation algorithms Approximation methods Computation Convergence Descent Eigenvalues and eigenfunctions Heuristic large-scale optimization Linear programming Mathematical analysis Mathematical models Optimization Quasi-Newton methods Signal processing algorithms stochastic optimization Stochasticity support vector machines
ISSN:	1053-587X, 1941-0476
On-line prístup:	Získať plný text
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