Complementary composite minimization, small gradients in general norms, and applications

Composite minimization is a powerful framework in large-scale convex optimization, based on decoupling of the objective function into terms with structurally different properties and allowing for more flexible algorithmic design. We introduce a new algorithmic framework for complementary composite m...

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Bibliographic Details
Published in:	Mathematical programming Vol. 208; no. 1-2; pp. 319 - 363
Main Authors:	Diakonikolas, Jelena, Guzmán, Cristóbal
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024 Springer
Subjects:	Algorithms Calculus of Variations and Optimal Control; Optimization Combinatorics Full Length Paper Machine learning Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Numerical Analysis Theoretical Linear convergence 65K05 Composite minimization 90C25 Regression 90C06 Gradient norm minimization
ISSN:	0025-5610, 1436-4646
Online Access:	Get full text
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