General inertial proximal stochastic mirror descent algorithm beyond Lipschitz smoothness assumption

In this paper, minimizing the sum of an average of finite proper closed nonconvex functions and a proper lower semicontinuous convex function over a closed convex set, is considered. We propose the general inertial proximal stochastic mirror descent (IPSMD for short) algorithm framework, which not o...

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Vydané v:	Journal of computational and applied mathematics Ročník 476; s. 117108
Hlavní autori:	Wang, Shuang, Dong, Xiaomei, Gao, Xue
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.04.2026
Predmet:	Inertial Mirror descent Nonconvex nonsmooth optimization Proximal gradient descent Variance reduction Mirror descent 49J52 Variance reduction Nonconvex nonsmooth optimization 90C26 90C15 Inertial Proximal gradient descent
ISSN:	0377-0427
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