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Globally Convergent Limited Memory Bundle Algorithm for Nondifferentiable Programming subject to Box Constraints

Napsu Karmitsa, Marko M. Mäkelä, Globally Convergent Limited Memory Bundle Algorithm for Nondifferentiable Programming subject to Box Constraints. TUCS Technical Reports 882, Turku Centre for Computer Science, 2008.

Abstract:

Practical optimization problems often involve nonsmooth functions of hundreds or thousands of variables. As a rule, the variables in such problems are restricted to certain meaningful intervals. In the report [Haarala, Mäkelä, 2006] we have described an efficient adaptive limited memory bundle method for large-scale nonsmooth, possibly nonconvex, box constrained optimization. In this paper, a new variant of this method is proposed and its global convergence for locally Lipschitz continuous functions is proved. In addition, some numerical experiments are given in order to show the applicability of the method.

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BibTeX entry:

@TECHREPORT{tKaMa08a,
  title = {Globally Convergent Limited Memory Bundle Algorithm for Nondifferentiable Programming subject to Box Constraints},
  author = {Karmitsa, Napsu and Mäkelä, Marko M.},
  number = {882},
  series = {TUCS Technical Reports},
  publisher = {Turku Centre for Computer Science},
  year = {2008},
  keywords = {Nonsmooth optimization, large-scale problems, bundle methods, limited memory methods, bound constraints, global convergence.},
  ISBN = {978-952-12-2063-0},
}

Belongs to TUCS Research Unit(s): Other

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