location: Current position: Home >> Scientific Research >> Paper Publications

An Approximate Redistributed Proximal Bundle Method with Inexact Data for Minimizing Nonsmooth Nonconvex Functions

Hits:

Indexed by:期刊论文

Date of Publication:2015-01-01

Journal:MATHEMATICAL PROBLEMS IN ENGINEERING

Included Journals:SCIE、EI、Scopus

Volume:2015

ISSN No.:1024-123X

Abstract:We describe an extension of the redistributed technique form classical proximal bundle method to the inexact situation for minimizing nonsmooth nonconvex functions. The cutting-planes model we construct is not the approximation to the whole nonconvex function, but to the local convexification of the approximate objective function, and this kind of local convexification is modified dynamically in order to always yield nonnegative linearization errors. Since we only employ the approximate function values and approximate subgradients, theoretical convergence analysis shows that an approximate stationary point or some double approximate stationary point can be obtained under some mild conditions.

Pre One:NONLINEAR LAGRANGIANS FOR NONLINEAR PROGRAMMING BASED ON MODIFIED FISCHER-BURMEISTER NCP FUNCTIONS

Next One:Several Classes of Polynomial-Time Solvable Fuzzy Relational Inequalities