The method of generalized stochastic gradient for solving minimax problems with constrained variables

DOI10.1016/0041-5553(90)90084-6zbMath0729.90077OpenAlexW1975874708MaRDI QIDQ3354477

A. G. Perevozchikov, S. K. Zavriev

Publication date: 1990

Published in: USSR Computational Mathematics and Mathematical Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0041-5553(90)90084-6

zbMATH Keywords

convergencedirect Lyapunov methodstochastic algorithmregularity theoremClarke's differentialgeneral constrained minimax programset of subgradients

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Nonsmooth analysis (49J52) Stochastic programming (90C15) Existence of solutions for minimax problems (49J35) Computational methods for problems pertaining to operations research and mathematical programming (90-08)

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