New variable-metric algorithms for nondifferentiable optimization problems

DOI10.1007/BF00939925zbMath0793.90078MaRDI QIDQ1321115

Stanislav P. Uryasev

Publication date: 27 April 1994

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

zbMATH Keywords

nonsmooth optimizationadaptive algorithmsnondifferentiable programmingvariable-metric algorithms

Mathematics Subject Classification ID

Nonlinear programming (90C30) Nonsmooth analysis (49J52)

Related Items (9)

Comparing different nonsmooth minimization methods and software ⋮ Survey of Bundle Methods for Nonsmooth Optimization ⋮ A quasi-Newton proximal bundle method using gradient sampling technique for minimizing nonsmooth convex functions ⋮ Stochastic perturbation of subgradient algorithm for nonconvex deep neural networks ⋮ Diagonal bundle method for nonsmooth sparse optimization ⋮ Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization ⋮ Subgradient and Bundle Methods for Nonsmooth Optimization ⋮ Random perturbation of the projected variable metric method for nonsmooth nonconvex optimization problems with linear constraints ⋮ Limited memory discrete gradient bundle method for nonsmooth derivative-free optimization

Cites Work

• Methods of descent for nondifferentiable optimization
• A modification and an extension of Lemarechal’s algorithm for nonsmooth minimization
• Quasi-Newton Methods, Motivation and Theory
• Convex Analysis
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: New variable-metric algorithms for nondifferentiable optimization problems