Ensemble-based gradient inference for particle methods in optimization and sampling

• URL: http://arxiv.org/abs/2209.15420v1
• Date: Fri, 23 Sep 2022 09:21:35 GMT
• Title: Ensemble-based gradient inference for particle methods in optimization and sampling
• Authors: Claudia Schillings and Claudia Totzeck and Philipp Wacker
• Abstract summary: We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information. We suggest to use this information for the improvement of established ensemble-based numerical methods for optimization and sampling.
• Score: 2.9005223064604078
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information of objective functions {from a given ensemble of particles}. Pointwise evaluation $\{V(x^i)\}_i$ of some potential $V$ in an ensemble $\{x^i\}_i$ contains implicit information about first or higher order derivatives, which can be made explicit with little computational effort (ensemble-based gradient inference -- EGI). We suggest to use this information for the improvement of established ensemble-based numerical methods for optimization and sampling such as Consensus-based optimization and Langevin-based samplers. Numerical studies indicate that the augmented algorithms are often superior to their gradient-free variants, in particular the augmented methods help the ensembles to escape their initial domain, to explore multimodal, non-Gaussian settings and to speed up the collapse at the end of optimization dynamics.} The code for the numerical examples in this manuscript can be found in the paper's Github repository (https://github.com/MercuryBench/ensemble-based-gradient.git).

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