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Adaptive Sampling Strategies for Stochastic Optimization

Raghu Bollapragada(raghu.bollapragada***at***u.northwestern.edu)
Richard Byrd(richardhbyrd***at***gmail.com)
Jorge Nocedal(j-nocedal***at***northwestern.edu)

Abstract: In this paper, we propose a stochastic optimization method that adaptively controls the sample size used in the computation of gradient approximations. Unlike other variance reduction techniques that either require additional storage or the regular computation of full gradients, the proposed method reduces variance by increasing the sample size as needed. The decision to increase the sample size is governed by an inner product test that ensures that search directions are descent directions with high probability. We show that the inner product test improves upon the well known norm test, and can be used as a basis for an algorithm that is globally convergent on nonconvex functions and enjoys a global linear rate of convergence on strongly convex functions. Numerical experiments on logistic regression problems illustrate the performance of the algorithm.

Keywords: Adaptive Sampling, Stochastic Optimization, Machine Learning

Category 1: Nonlinear Optimization

Category 2: Stochastic Programming

Category 3: Convex and Nonsmooth Optimization (Convex Optimization )

Citation:

Download: [PDF]

Entry Submitted: 10/30/2017
Entry Accepted: 10/30/2017
Entry Last Modified: 10/30/2017

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