Stochastic generalized-differentiable functions in the problem of nonconvex nonsmooth stochastic optimization

DOI10.1007/BF01068698zbMath0693.90074MaRDI QIDQ3468884

Vladimir I. Norkin

Publication date: 1986

Published in: Cybernetics (Search for Journal in Brave)

zbMATH Keywords

generalized differentiabilitypseudogradientnonconvex nonsmooth stochastic optimizationstochastic generalized-differentiable functions

Mathematics Subject Classification ID

Nonlinear programming (90C30) Nonsmooth analysis (49J52) Stochastic programming (90C15) Fréchet and Gateaux differentiability in optimization (49J50)

Related Items (6)

Conservative and semismooth derivatives are equivalent for semialgebraic maps ⋮ Subgradient Sampling for Nonsmooth Nonconvex Minimization ⋮ Generalized gradients in dynamic optimization, optimal control, and machine learning problems ⋮ Generalized gradient learning on time series ⋮ Stochastic generalized gradient methods for training nonconvex nonsmooth neural networks ⋮ Estimated stochastic programs with chance constraints

Cites Work

• Measurable dependence of convex sets and functions on parameters
• Stochastic Lipschitz functions
• Measurable relations
• Semismooth and Semiconvex Functions in Constrained Optimization
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