SABRINA: A Stochastic Subspace Majorization-Minimization Algorithm

A wide class of problems involves the minimization of a coercive and differentiable function F on R N whose gradient cannot be evaluated in an exact manner. In such context, many existing convergence results from standard gradient-based optimization literature cannot be directly applied and robustne...

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Vydáno v:	Journal of optimization theory and applications Ročník 195; číslo 3; s. 919 - 952
Hlavní autoři:	Chouzenoux, Emilie, Fest, Jean-Baptiste
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.12.2022 Springer Nature B.V Springer Verlag
Témata:	Algorithms Applications of Mathematics Calculus of Variations and Optimal Control; Optimization Convergence Engineering Engineering Sciences Errors Image processing Machine learning Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Robustness (mathematics) Signal and Image processing Stochastic processes Subspaces Theory of Computation Subspace acceleration Majorization-minimization Stochastic optimization Binary logistic regression Image reconstruction Convergence analysis subspace acceleration binary logistic regression image reconstruction convergence analysis Majorization-Minimization
ISSN:	0022-3239, 1573-2878
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