A decomposition method with redistributed subroutine for constrained nonconvex optimization
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.
superlinear convergent \(\mathcal{VU}\)-algorithm
scientific article; zbMATH DE number 6161327
scientific article