We introduce Envelopt, a globally convergent iterative framework for a broad class of structured optimization problems where a smooth objective is augmented by a nonsmooth convex regularizer composed with a smooth mapping, and the variables are subject to general smooth constraints. All smooth functions may be nonconve...
We study an inexact interior-point method for nonsmooth, nonconvex optimization problems with conic inequality constraints. The objective function is given by the sum of a smooth, possibly nonconvex term and a convex, possibly nonsmooth term with a computable proximal mapping. The constraints are formulated by means of...
The reduced linear system used to compute the Newton step is derived, the corresponding merit function is defined, and practical approaches for constructing the diagonal scaling matrix from derivative information are discussed.
E. Bertolazzi, Alberto De Marchi, Davide Stocco· 0 citations
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