On the convergence of conditional \(\varepsilon\)-subgradient methods for convex programs and convex-concave saddle-point problems.

DOI10.1016/S0377-2217(02)00629-XzbMath1053.90107OpenAlexW2011746047MaRDI QIDQ1410305

Torbjörn Larsson, Michael Patriksson, Ann-Brith Strömberg

Publication date: 14 October 2003

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0377-2217(02)00629-x

zbMATH Keywords

Convex programmingNonlinear programmingGame theoryLarge-scale optimization

Mathematics Subject Classification ID

Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Methods of reduced gradient type (90C52)

Related Items (10)

Inexact subgradient methods for quasi-convex optimization problems ⋮ On the convergence of primal-dual hybrid gradient algorithms for total variation image restoration ⋮ Incremental subgradient algorithms with dynamic step sizes for separable convex optimizations ⋮ Subgradient algorithms on Riemannian manifolds of lower bounded curvatures ⋮ Ergodic, primal convergence in dual subgradient schemes for convex programming. II: The case of inconsistent primal problems ⋮ A Subgradient Method Based on Gradient Sampling for Solving Convex Optimization Problems ⋮ Modified Fejér sequences and applications ⋮ A merit function approach to the subgradient method with averaging ⋮ Scaling Techniques for $\epsilon$-Subgradient Methods ⋮ Weak subgradient method for solving nonsmooth nonconvex optimization problems

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