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A decomposition method with redistributed subroutine for constrained nonconvex optimization. (English) Zbl 1270.90050

Summary: A class of constrained nonsmooth nonconvex optimization problems, that is, piecewise \(C^2\) objectives with smooth inequality constraints are discussed in this paper. Based on the \(\mathcal{VU}\)-theory, a superlinear convergent \(\mathcal{VU}\)-algorithm, which uses a nonconvex redistributed proximal bundle subroutine, is designed to solve these optimization problems. An illustrative example is given to show how this convergent method works on a second-order cone programming problem.

MSC:

90C26 Nonconvex programming, global optimization
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