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A derivative-free method for linearly constrained nonsmooth optimization
Centre for Informatics and Applied Optimization, School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Victoria, 3353
This paper develops a new derivative-free method for solving
linearly constrained nonsmooth optimization problems. The
objective functions in these problems are, in general, non-regular
locally Lipschitz continuous function. The computation of
generalized subgradients of such functions is difficult task. In
this paper we suggest an algorithm for the computation of
subgradients of a broad class of non-regular locally continuous
Lipschitz functions. This algorithm is based on the notion of a
discrete gradient. An algorithm for solving linearly constrained
nonsmooth optimization problems based on discrete gradients is
developed. We report preliminary results of numerical experiments.
These results demonstrate that the proposed algorithm is efficient
for solving linearly constrained nonsmooth optimization problems.