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arXiv · 2006.03949

SONIA: A Symmetric Blockwise Truncated Optimization Algorithm

Majid Jahani·Mohammadreza Nazari·Rachael Tappenden·Albert S. Berahas·Martin Takáč
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Abstract

This work presents a new algorithm for empirical risk minimization. The algorithm bridges the gap between first- and second-order methods by computing a search direction that uses a second-order-type update in one subspace, coupled with a scaled steepest descent step in the orthogonal complement. To this end, partial curvature information is incorporated to help with ill-conditioning, while simultaneously allowing the algorithm to scale to the large problem dimensions often encountered in machine learning applications. Theoretical results are presented to confirm that the algorithm converges to a stationary point in both the strongly convex and nonconvex cases. A stochastic variant of the algorithm is also presented, along with corresponding theoretical guarantees. Numerical results confirm the strengths of the new approach on standard machine learning problems.

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Majid Jahani, Mohammadreza Nazari, Rachael Tappenden, Albert S. Berahas, Martin Takáč. 2020-06-06. SONIA: A Symmetric Blockwise Truncated Optimization Algorithm. https://arxiv.org/abs/2006.03949

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