Weak convergence of inertial proximal algorithms with self adaptive stepsize for solving multivalued variational inequalities
Trần Văn Thắng, Nguyen Duc Hien, H. T. C. Thach, Phạm Ngọc Ánh
Abstract
In this work, we introduce an inertial proximal algorithm for solving multivalued variational inequality problems in a real Hilbert space. By using self-adaptive and inertial techniques via proximal operators, we establish the weak convergence of the iteration sequences generated by these algorithms when the multivalued cost mappings associated with the problems are monotone and Lipschitz continuous. Moreover, we present the nonasymptotic O(1k) convergence rate of the proposed algorithms. We also provide some numerical examples to illustrate the accuracy and efficiency of our algorithms by comparing with other recent algorithms in the literature.
Topics & Concepts
MathematicsVariational inequalityConvergence (economics)Inertial frame of referenceApplied mathematicsAlgorithmWeak convergenceMathematical analysisClassical mechanicsComputer sciencePhysicsEconomic growthComputer securityEconomicsAsset (computer security)Optimization and Variational AnalysisAdvanced Optimization Algorithms ResearchNumerical methods in inverse problems