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Minimizing finite sums with the stochastic average gradient. (English) Zbl 1358.90073
Math. Program. 162, No. 1-2 (A), 83-112 (2017); erratum ibid. 162, No. 1-2 (A), 113 (2017).
Summary: We analyze the stochastic average gradient (SAG) method for optimizing the sum of a finite number of smooth convex functions. Like stochastic gradient (SG) methods, the SAG method’s iteration cost is independent of the number of terms in the sum. However, by incorporating a memory of previous gradient values the SAG method achieves a faster convergence rate than black-box SG methods. The convergence rate is improved from \(O(1/\sqrt{k})\) to \(O(1/k)\) in general, and when the sum is strongly-convex the convergence rate is improved from the sub-linear \(O(1/k)\) to a linear convergence rate of the form \(O(\rho ^k)\) for \(\rho < 1\). Further, in many cases the convergence rate of the new method is also faster than black-box deterministic gradient methods, in terms of the number of gradient evaluations. This extends our earlier work [the second author et al., “A stochastic gradient method with an exponential convergence rate for strongly-convex optimization with finite training sets”, in: Advances in Neural Information Processing Systems. Proceedings of the first 12 conferences. Cambridge, MA: The MIT Press. 2663–2671 (2012)], which only lead to a faster rate for well-conditioned strongly-convex problems. Numerical experiments indicate that the new algorithm often dramatically outperforms existing SG and deterministic gradient methods, and that the performance may be further improved through the use of non-uniform sampling strategies.

90C06 Large-scale problems in mathematical programming
90C15 Stochastic programming
90C25 Convex programming
90C30 Nonlinear programming
65K05 Numerical mathematical programming methods
68Q25 Analysis of algorithms and problem complexity
62L20 Stochastic approximation
Full Text: DOI
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