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arXiv · 1709.01434

A Generic Approach for Escaping Saddle points

Sashank J Reddi·Manzil Zaheer·Suvrit Sra·Barnabas Poczos·Francis Bach·Ruslan Salakhutdinov·Alexander J Smola
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Abstract

A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly deteriorating their performance. Typically, to escape from saddles one has to use second-order methods. However, most works on second-order methods rely extensively on expensive Hessian-based computations, making them impractical in large-scale settings. To tackle this challenge, we introduce a generic framework that minimizes Hessian based computations while at the same time provably converging to second-order critical points. Our framework carefully alternates between a first-order and a second-order subroutine, using the latter only close to saddle points, and yields convergence results competitive to the state-of-the-art. Empirical results suggest that our strategy also enjoys a good practical performance.

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Sashank J Reddi, Manzil Zaheer, Suvrit Sra, Barnabas Poczos, Francis Bach, Ruslan Salakhutdinov, Alexander J Smola. 2017-09-05. A Generic Approach for Escaping Saddle points. https://arxiv.org/abs/1709.01434

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