A Smoothing Method for Sparse Programs by Symmetric Cone Constrained Generalized Equations

In this paper, we consider a sparse program with symmetric cone constrained parameterized generalized equations (SPSCC). Such problem is analogue vector optimization, and aim to provide smoothing framework for dealing SPSCC that includes classical complementarity problems the nonnegative cone, semidefinite second-order cone. An effective approximation given focus on solving perturbation problem. The necessary optimality conditions, which are reformulated as system of nonsmooth equations, sufficient conditions proposed. Under mild Newton approach used solve these equations. strong BD-regularity at solution point can be satisfied. inverse linear provided discussed an illustrative example, verified efficiency proposed algorithm.