An ODE method to prove the geometric convergence of adaptive stochastic algorithms

DOI10.1016/j.spa.2021.12.005zbMath1485.90128arXiv1811.06703OpenAlexW2900724771MaRDI QIDQ2074991

Anne Auger, Nikolaus Hansen, Youhei Akimoto

Publication date: 11 February 2022

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1811.06703

zbMATH Keywords

optimizationLyapunov stabilitygeometric convergenceordinary differential equation methodadaptive stochastic algorithmcomparison-based algorithm

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Nonlinear programming (90C30) Stochastic programming (90C15)

Related Items

Global linear convergence of evolution strategies with recombination on scaling-invariant functions, Analysis of surrogate-assisted information-geometric optimization algorithms

Uses Software

• CMA-ES

Cites Work

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