A trust region method using subgradient for minimizing a nondifferentiable function

Authors

  • Milanka Gardašević-Filipović Faculty of Architecture, Belgrade

DOI:

https://doi.org/10.2298/YJOR0902249G

Keywords:

Trust region method, non-smooth convex optimization

Abstract

The minimization of a particular nondifferentiable function is considered. The first and second order necessary conditions are given. A trust region method for minimization of this form of the objective function is presented. The algorithm uses the subgradient instead of the gradient. It is proved that the sequence of points generated by the algorithm has an accumulation point which satisfies the first and second order necessary conditions.

References

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Fletcher, R. (1980) Practical methods of optimization. New York, itd: Wiley, vol. I: Unconstrained optimization

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Fuduli, A. (1998) Metodi numerici per la minimizzazione di funzioni convesse nondifferenziabili. Calabria: Departimento di Electronica Informatica e Sistemistica, PhD Thesis

Gertz, E.M., Gill, P.E. (2004) A primal-dual trust-region algorithm for nonlinear optimization. Math. Program, Ser B, 100, 49-94

Kusarev, A.G., Kutateladze, S.S. (1987) Subdifferencial'noe isčislenie. Novosibirsk: Izdate'stvo Nauka

Pšeničnj, B.N. (1983) Metod linearizacii. Moskva: Nauka

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Published

2009-09-01

Issue

Vol. 19 No. 2 (2009)

Section

Research Articles

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