A family of subgradient-based methods for convex optimization problems in a unifying framework

DOI10.1080/10556788.2016.1182165zbMath1355.90065arXiv1403.6526OpenAlexW2275286187MaRDI QIDQ2829570

Masaru Ito, Mituhiro Fukuda

Publication date: 8 November 2016

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1403.6526

zbMATH Keywords

complexity boundsnon-smooth/smooth convex optimizationstructured convex optimizationsubgradient/gradient-based proximal methodmirror-descent methoddual-averaging method

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Convex programming (90C25) Numerical methods based on nonlinear programming (49M37) Methods of reduced gradient type (90C52)

Related Items (3)

New results on subgradient methods for strongly convex optimization problems with a unified analysis ⋮ On the computational efficiency of subgradient methods: a case study with Lagrangian bounds ⋮ Gradient projection methods for the $n$-coupling problem

Cites Work

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