Dynamic smoothness parameter for fast gradient methods

DOI10.1007/s11590-017-1168-zzbMath1391.90474OpenAlexW2609009132WikidataQ118165417 ScholiaQ118165417MaRDI QIDQ683921

Bernard Gendron, Enrico Gorgone, Antonio Frangioni

Publication date: 9 February 2018

Published in: Optimization Letters (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11584/218770

zbMATH Keywords

Lagrangian relaxationconvex optimizationfast gradient method

Mathematics Subject Classification ID

Convex programming (90C25) Methods of reduced gradient type (90C52)

Related Items

Essentials of numerical nonsmooth optimization, Essentials of numerical nonsmooth optimization

Cites Work

• Unnamed Item
• Unnamed Item
• Smooth minimization of non-smooth functions
• Optimal subgradient algorithms for large-scale convex optimization in simple domains
• On the computational efficiency of subgradient methods: a case study with Lagrangian bounds
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Bundle methods for sum-functions with ``easy components: applications to multicommodity network design
• A variable smoothing algorithm for solving convex optimization problems
• Incremental Subgradient Methods for Nondifferentiable Optimization
• Smoothing and First Order Methods: A Unified Framework
• Generalized Bundle Methods
• Convergence Analysis of Deflected Conditional Approximate Subgradient Methods