A Global Stochastic Optimization Particle Filter Algorithm

• URL: http://arxiv.org/abs/2007.04803v9
• Date: Wed, 6 Jul 2022 06:41:47 GMT
• Title: A Global Stochastic Optimization Particle Filter Algorithm
• Authors: Mathieu Gerber and Randal Douc
• Abstract summary: We introduce a new algorithm for expected log-likelihood in situations where the objective function is multi-modal. With high probability, G-PFSO successfully finds the objective function and converges to its global maximizer at the optimal rate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new online algorithm for expected log-likelihood maximization in situations where the objective function is multi-modal and/or has saddle points, that we term G-PFSO. The key element underpinning G-PFSO is a probability distribution which (a) is shown to concentrate on the target parameter value as the sample size increases and (b) can be efficiently estimated by means of a standard particle filter algorithm. This distribution depends on a learning rate, where the faster the learning rate the quicker it concentrates on the desired element of the search space, but the less likely G-PFSO is to escape from a local optimum of the objective function. In order to achieve a fast convergence rate with a slow learning rate, G-PFSO exploits the acceleration property of averaging, well-known in the stochastic gradient literature. Considering several challenging estimation problems, the numerical experiments show that, with high probability, G-PFSO successfully finds the highest mode of the objective function and converges to its global maximizer at the optimal rate. While the focus of this work is expected log-likelihood maximization, the proposed methodology and its theory apply more generally for optimizing a function defined through an expectation.

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