Convergence of iterative processes in nonlinear optimization

DOI10.1007/BF00934440zbMath0497.49025OpenAlexW2038427910MaRDI QIDQ1170760

S. Singh

Publication date: 1983

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

Full work available at URL: https://doi.org/10.1007/bf00934440

zbMATH Keywords

maximal monotone operatorsstochastic approximationscontraction mappingsprobabilistic convergenceconvex functionalsconvergence of a class of iterative processes

Mathematics Subject Classification ID

Nonlinear programming (90C30) Monotone operators and generalizations (47H05) Numerical methods based on nonlinear programming (49M37) Contraction-type mappings, nonexpansive mappings, (A)-proper mappings, etc. (47H09) Stochastic approximation (62L20) Convergence of probability measures (60B10) Methods of reduced gradient type (90C52)

Cites Work

• Monotone (nonlinear) operators in Hilbert space
• Difference approximation of Cauchy problems for quasi-dissipative operators and generation of nonlinear semigroups
• On the range of accretive operators
• A strongly convergent iterative solution of \(0 \in U(x)\) for a maximal monotone operator U in Hilbert space
• Constructing zeros of accretive operators II
• Monotone Operators and the Proximal Point Algorithm
• On Dvoretzky Stochastic Approximation Theorems
• A Stochastic Approximation Method

This page was built for publication: Convergence of iterative processes in nonlinear optimization