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arXiv · 1710.10551

Stochastic Zeroth-order Optimization in High Dimensions

Yining Wang·Simon Du·Sivaraman Balakrishnan·Aarti Singh
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Abstract

We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries. Under sparsity assumptions on the gradients or function values, we present two algorithms: a successive component/feature selection algorithm and a noisy mirror descent algorithm using Lasso gradient estimates, and show that both algorithms have convergence rates that de- pend only logarithmically on the ambient dimension of the problem. Empirical results confirm our theoretical findings and show that the algorithms we design outperform classical zeroth-order optimization methods in the high-dimensional setting.

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Yining Wang, Simon Du, Sivaraman Balakrishnan, Aarti Singh. 2017-10-29. Stochastic Zeroth-order Optimization in High Dimensions. https://arxiv.org/abs/1710.10551

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