Convergence Rate Analysis of a Class of Uncertain Variational Inequalities

Variational inequalities theory is a potent tool that can be employed to tackle diverse optimization problems, with applications spanning physics, economics, finance and beyond. Solving the uncertain variational (UVI) has emerged as prominent research topic in academia. However, conventional regularization methods for solving UVI are subject certain limitations on convergence rate of algorithm. To overcome this challenge, we opt class regularized gap functions which lower semi-continuous $\phi $ -bounded true convex properties. We establish an expected value equivalent model demonstrate exponential function. Furthermore, investigate subdifferential (LSTCOS) develop deviation between LSTCOS its closed hull. By utilizing their deviations, transform solution uniform LSTCOS. Finally, prove uniformly converges at based generalized equation.