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OSGA: A fast subgradient algorithm with optimal complexity

Arnold Neumaier (Arnold.Neumaier***at***univie.ac.at)

Abstract: This paper presents an algorithm for approximately minimizing a convex function in simple, not necessarily bounded convex domains, assuming only that function values and subgradients are available. No global information about the objective function is needed apart from a strong convexity parameter (which can be put to zero if only convexity is known). The worst case number of iterations needed to achieve a given accuracy is independent of the dimension (which may be infinite) and - apart from a constant factor - best possible under a variety of smoothness assumptions on the objective function.

Keywords: complexity bound, convex optimization, optimal subgradient method, large-scale optimization, Nesterov's optimal method, nonsmooth optimization, optimal first-order method, smooth optimization, strongly convex

Category 1: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Citation:

Download: [PDF]

Entry Submitted: 02/05/2014
Entry Accepted: 02/05/2014
Entry Last Modified: 02/05/2014

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