Limited memory interior point bundle method for large inequality constrained nonsmooth minimization

Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables with various constraints. In this paper, we describe a new efficient adaptive limited memory interior point bundle method for large, possible nonconvex, no...

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Bibliographic Details
Published in:Applied mathematics and computation 2008-04, Vol.198 (1), p.382-400
Main Authors: Karmitsa, N.M.S., Mäkelä, M.M., Ali, M.M.
Format: Article
Language:English
Subjects:
Bundle methods
Constrained optimization
Exact sciences and technology
Feasible direction interior point methods
Large-scale problems
Limited memory methods
Mathematical analysis
Mathematics
Nondifferentiable programming
Numerical analysis
Numerical analysis. Scientific computation
Sciences and techniques of general use
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Summary:Many practical optimization problems involve nonsmooth (that is, not necessarily differentiable) functions of hundreds or thousands of variables with various constraints. In this paper, we describe a new efficient adaptive limited memory interior point bundle method for large, possible nonconvex, nonsmooth inequality constrained optimization. The method is a hybrid of the nonsmooth variable metric bundle method and the smooth limited memory variable metric method, and the constraint handling is based on the primal–dual feasible direction interior point approach. The preliminary numerical experiments to be presented confirm the effectiveness of the method.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2007.08.044