A stochastic algorithm using one sample point per iteration and diminishing step-sizes

DOI10.1007/BF00941830zbMath0644.90079OpenAlexW2038084402MaRDI QIDQ1102867

B. George

Publication date: 1989

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00941830

zbMATH Keywords

supermartingalesMonte Carlo simulationsstationary pointsstochastic algorithmdescent directionRobbins-Monro stochastic approximationgradient evaluations

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)

Related Items (3)

On a proof of Robbins-Monro algorithm ⋮ Stochastic proximal gradient methods for nonconvex problems in Hilbert spaces ⋮ Stochastic algorithms with Armijo stepsizes for minimization of functions

Cites Work

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• A stochastic algorithm for optimization problems with continua of inequalities
• A stochastic steepest-descent algorithm
• Stochastic approximation algorithms for constrained optimization problems
• Minimization of functions having Lipschitz continuous first partial derivatives
• Stochastic approximation algorithms for the local optimization of functions with nonunique stationary points
• Stochastic approximation type methods for constrained systems: Algorithms and numerical results
• Stochastic Estimation of the Maximum of a Regression Function
• A Stochastic Approximation Method

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