An Approximate Quasi-Newton Bundle-Type Method for Nonsmooth Optimization | Zendy

Jie Shen | Zendy; Li-Ping Pang | Zendy; Dan Li | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

An Approximate Quasi-Newton Bundle-Type Method for Nonsmooth Optimization

Author(s) -

Jie Shen,

Li-Ping Pang,

Dan Li

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/697474

Subject(s) - mathematics , regularization (linguistics) , bundle , rate of convergence , newton's method , convergence (economics) , type (biology) , convex function , mathematical optimization , convex optimization , regular polygon , nonlinear system , computer science , geometry , key (lock) , quantum mechanics , artificial intelligence , economics , composite material , biology , economic growth , ecology , materials science , physics , computer security

An implementable algorithm for solving a nonsmooth convex optimization problem is proposed by combining Moreau-Yosida regularization and bundle and quasi-Newton ideas. In contrast with quasi-Newton bundle methods of Mifflin et al. (1998), we only assume that the values of the objective function and its subgradients are evaluated approximately, which makes the method easier to implement. Under some reasonable assumptions, the proposed method is shown to have a Q-superlinear rate of convergence. © 2013 Jie Shen et al.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research