The CoMirror algorithm for solving nonsmooth constrained convex problems

DOI10.1016/j.orl.2010.08.005zbMath1202.90209OpenAlexW2016760858MaRDI QIDQ614018

Amir Beck, Aharon Ben-Tal, Nili Guttmann-Beck, Luba Tetruashvili

Publication date: 23 December 2010

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.orl.2010.08.005

zbMATH Keywords

convex optimizationmirror descentgradient-based methodsnon-Euclidean projection

Mathematics Subject Classification ID

Convex programming (90C25)

Related Items

The CoMirror algorithm with random constraint sampling for convex semi-infinite programming, OSGA: a fast subgradient algorithm with optimal complexity, A dual method for minimizing a nonsmooth objective over one smooth inequality constraint, A derivative-free comirror algorithm for convex optimization, Algorithms for stochastic optimization with function or expectation constraints, Primal-dual mirror descent method for constraint stochastic optimization problems, Online First-Order Framework for Robust Convex Optimization, FOM – a MATLAB toolbox of first-order methods for solving convex optimization problems, Solving structured nonsmooth convex optimization with complexity \(\mathcal {O}(\varepsilon ^{-1/2})\), Analogues of Switching Subgradient Schemes for Relatively Lipschitz-Continuous Convex Programming Problems, Adaptive Mirror Descent Algorithms for Convex and Strongly Convex Optimization Problems with Functional Constraints, A class of exact penalty functions and penalty algorithms for nonsmooth constrained optimization problems, On Modification of an Adaptive Stochastic Mirror Descent Algorithm for Convex Optimization Problems with Functional Constraints

Uses Software

• Regularization tools
• SDPT3
• CVX

Cites Work

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