Adaptive State-Dependent Diffusion for Derivative-Free Optimization

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外文摘要：This paper develops and analyzes a stochastic derivative-free optimization strategy.A key feature is the state-dependent adaptive variance.We prove global convergence in probability with alge-braic rate and give the quantitative results in numerical examples.A striking fact is that conver-gence is achieved without explicit information of the gradient and even without comparing differ-ent objective function values as in established methods such as the simplex method and simulated annealing.It can otherwise be compared to annealing with state-dependent temperature.

外文关键词：

Derivative-free optimizationGlobal optimizationAdaptive diffusionStationary distributionFokker-Planck theory

作者：

Bj?rn Engquist、Kui Ren、Yunan Yang

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作者单位：

Department of Mathematics and the Oden Institute,The University of Texas,Austin,TX 78712,USA

Department of Applied Physics and Applied Mathematics,Columbia University,New York,NY 10027,USA

Department of Mathematics,Cornell University,Ithaca,NY 14853,USA

基金：

National Science FoundationNational Science FoundationNational Science FoundationNational Science FoundationDr.Max R?ssler,the Walter Haefner FoundationETH Zürich Foundation

项目编号：

DMS-2208504BEDMS-1913309KRDMS-1937254KRDMS-1913129YY

出版年：

2024

DOI：

10.1007/s42967-023-00324-3

应用数学与计算数学学报

上海大学

应用数学与计算数学学报

影响因子：0.165

ISSN：1006-6330

年,卷(期)：2024.6(2)