“School of Mathematics”
Back to Papers HomeBack to Papers of School of Mathematics
| Paper IPM / M / 17409 |
|
| Abstract: | |
|
n this paper, we focus on a descent algorithm for solving nonsmooth nonconvex opti-
mization problems. The proposed method is based on the proximal bundle algorithm
and the gradient sampling method and uses the advantages of both. In addition, this
algorithm has the ability to handle inexact information, which creates additional chal-
lenges. The global convergence is proved with probability one. More precisely, every
accumulation point of the sequence of serious iterates is either a stationary point if
exact values of gradient are provided or an approximate stationary point if only inex-
act information of the function and gradient values is available. The performance of
the proposed algorithm is demonstrated using some academic test problems. We fur-
ther compare the new method with a general nonlinear solver and two other methods
specifically designed for nonconvex nonsmooth optimization problems
Download TeX format |
|
| back to top | |
