A New Complementarity Function and Applications in Stochastic Second-Order Cone Complementarity Problems

This paper considers the so-called expected residual minimization (ERM) formulation for stochastic second-order cone complementarity problems, which is based on a new complementarity function called termwise residual complementarity function associated with second-order cone. We show that the ERM model has bounded level sets under the stochastic weak R0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document}-property. We further derive some error bound results under either the strong monotonicity or some kind of constraint qualifications. Then, we apply the Monte Carlo approximation techniques to solve the ERM model and establish a comprehensive convergence analysis. Furthermore, we report some numerical results on a stochastic second-order cone model for optimal power flow in radial networks.