Subgradient and Bundle Methods for Nonsmooth Optimization

DOI10.1007/978-94-007-5288-7_15zbMath1267.65066OpenAlexW176594001MaRDI QIDQ2838345

Napsu Karmitsa, Marko M. Mäkelä, Adil M. Bagirov

Publication date: 9 July 2013

Published in: Computational Methods in Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-94-007-5288-7_15

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05)

Related Items (2)

A Nonsmooth Trust-Region Method for Locally Lipschitz Functions with Application to Optimization Problems Constrained by Variational Inequalities ⋮ Resource allocation for contingency planning: an inexact proximal bundle method for stochastic optimization

Uses Software

• SolvOpt
• UFO
• GradSamp
• PBNCGC
• PNEW
• DGM
• QSM
• LDGB
• MPBNGC

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Optimization of upper semidifferentiable functions
• Globally convergent limited memory bundle method for large-scale nonsmooth optimization
• Proximity control in bundle methods for convex nondifferentiable minimization
• Discrete gradient method: Derivative-free method for nonsmooth optimization
• Nonsmooth approach to optimization problems with equilibrium constraints. Theory, applications and numerical results
• Nonconvex optimization in mechanics. Algorithms, heuristics and engineering applications by the F. E. M
• A bundle-Newton method for nonsmooth unconstrained minimization
• Comparing nonsmooth nonconvex bundle methods in solving hemivariational inequalities
• New variable-metric algorithms for nondifferentiable optimization problems
• Representations of quasi-Newton matrices and their use in limited memory methods
• An implementation of Shor's \(r\)-algorithm
• A method of truncated codifferential with application to some problems of cluster analysis
• Non-Euclidean restricted memory level method for large-scale convex optimization
• Linear convergence of epsilon-subgradient descent methods for a class of convex functions
• Mirror descent and nonlinear projected subgradient methods for convex optimization.
• Methods of descent for nondifferentiable optimization
• Comparing different nonsmooth minimization methods and software
• A quasisecant method for minimizing nonsmooth functions
• Optimization and nonsmooth analysis
• A Version of the Bundle Idea for Minimizing a Nonsmooth Function: Conceptual Idea, Convergence Analysis, Numerical Results
• Variants to the cutting plane approach for convex nondifferentiable optimization
• Survey of Bundle Methods for Nonsmooth Optimization
• Robust Formulations for Training Multilayer Perceptrons
• Mathematical Programming for Data Mining: Formulations and Challenges
• A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
• An Infeasible Bundle Method for Nonsmooth Convex Constrained Optimization without a Penalty Function or a Filter
• New limited memory bundle method for large-scale nonsmooth optimization
• Algorithm 811: NDA
• Gobally convergent variable metric method for nonconvex nondifferentiable unconstrained minimization
• Benchmarking optimization software with performance profiles.

This page was built for publication: Subgradient and Bundle Methods for Nonsmooth Optimization