A superlinear space decomposition algorithm for constrained nonsmooth convex program

DOI10.1016/j.cam.2009.12.018zbMath1226.65056OpenAlexW1999818756MaRDI QIDQ964944

Li-Ping Pang, Fang-Fang Guo, Yuan Lu, Zun-Quan Xia

Publication date: 21 April 2010

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2009.12.018

zbMATH Keywords

nonsmooth optimizationconvex optimizationnumerical examplessecond-order expansionlocal superlinear convergence\(\mathcal {VU}\) decompositionpiecewise \(C^{2}\)primal-dual gradient structure

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25)

Related Items

A redistributed proximal bundle method for nonsmooth nonconvex functions with inexact information ⋮ A decomposition method with redistributed subroutine for constrained nonconvex optimization ⋮ The space decomposition theory for a class of eigenvalue optimizations ⋮ Stochastic methods based on \(\mathcal{VU}\)-decomposition methods for stochastic convex minimax problems ⋮ The space decomposition theory for a class of semi-infinite maximum eigenvalue optimizations

Cites Work

• A \(\mathcal{VU}\)-algorithm for convex minimization
• On $\mathcalVU$-theory for Functions with Primal-Dual Gradient Structure
• Primal-Dual Gradient Structured Functions: Second-Order Results; Links to Epi-Derivatives and Partly Smooth Functions
• 𝒱𝒰-smoothness and proximal point results for some nonconvex functions
• On Second-Order Properties of the Moreau–Yosida Regularization for Constrained Nonsmooth Convex Programs
• The 𝒰-Lagrangian of a convex function
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