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arXiv · 1909.01238

Stochastic quasi-Newton with line-search regularization

Adrian Wills·Thomas Schön
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Abstract

In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation. Quasi-Newton methods have had an enormous impact on deterministic optimisation problems because they afford rapid convergence and computationally attractive algorithms. In essence, this is achieved by learning the second-order (Hessian) information based on observing first-order gradients. We extend these ideas to the stochastic setting by employing a highly flexible model for the Hessian and infer its value based on observing noisy gradients. In addition, we propose a stochastic counterpart to standard line-search procedures and demonstrate the utility of this combination on maximum likelihood identification for general nonlinear state space models.

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Adrian Wills, Thomas Schön. 2019-09-03. Stochastic quasi-Newton with line-search regularization. https://arxiv.org/abs/1909.01238

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