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arXiv · 1402.1125

OSGA: A fast subgradient algorithm with optimal complexity

Arnold Neumaier
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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.

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Arnold Neumaier. 2014-02-05. OSGA: A fast subgradient algorithm with optimal complexity. https://arxiv.org/abs/1402.1125

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