An existence-uniqueness theorem and alternating contraction projection methods for inverse variational inequalities

Abstract

Let C be a nonempty closed convex subset of a real Hilbert space mathcal{H} with inner product langle cdot , cdot rangle , and let f: mathcal{H}rightarrow mathcal{H} be a nonlinear operator. Consider the inverse variational inequality (in short, operatorname{IVI}(C,f)) problem of finding a point xi ^{*}in mathcal{H} such that \t\t\tf(ξ∗)∈C,〈ξ∗,v−f(ξ∗)〉≥0,∀v∈C.\\documentclass[12pt]{minimal}\t\t\t\t\\usepackage{amsmath}\t\t\t\t\\usepackage{wasysym}\t\t\t\t\\usepackage{amsfonts}\t\t\t\t\\usepackage{amssymb}\t\t\t\t\\usepackage{amsbsy}\t\t\t\t\\usepackage{mathrsfs}\t\t\t\t\\usepackage{upgreek}\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\t\t\t\t\\begin{document}$$ f\\bigl(\\xi ^{*}\\bigr)\\in C, \\quad \\bigl\\langle \\xi ^{*}, v-f \\bigl(\\xi ^{*}\\bigr)\\bigr\\rangle \\geq 0, \\quad \\forall v\\in C. $$\\end{document} In this paper, we prove that operatorname{IVI}(C,f) has a unique solution if f is Lipschitz continuous and strongly monotone, which essentially improves the relevant result in (Luo and Yang in Optim. Lett. 8:1261–1272, 2014). Based on this result, an iterative algorithm, named the alternating contraction projection method (ACPM), is proposed for solving Lipschitz continuous and strongly monotone inverse variational inequalities. The strong convergence of the ACPM is proved and the convergence rate estimate is obtained. Furthermore, for the case that the structure of C is very complex and the projection operator P_{C} is difficult to calculate, we introduce the alternating contraction relaxation projection method (ACRPM) and prove its strong convergence. Some numerical experiments are provided to show the practicability and effectiveness of our algorithms. Our results in this paper extend and improve the related existing results.