Back to Search
Start Over
Globally convergent limited memory bundle method for large-scale nonsmooth optimization.
- Source :
- Mathematical Programming; Jan2007, Vol. 109 Issue 1, p181-205, 25p, 1 Diagram, 4 Charts, 2 Graphs
- Publication Year :
- 2007
-
Abstract
- Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of thousands of variables. In the paper [Haarala, Miettinen, Mäkelä, Optimization Methods and Software, 19, (2004), pp. 673–692] we have described an efficient method for large-scale nonsmooth optimization. In this paper, we introduce a new variant of this method and prove its global convergence for locally Lipschitz continuous objective functions, which are not necessarily differentiable or convex. In addition, we give some encouraging results from numerical experiments. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00255610
- Volume :
- 109
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Mathematical Programming
- Publication Type :
- Academic Journal
- Accession number :
- 23268494
- Full Text :
- https://doi.org/10.1007/s10107-006-0728-2