Gap Functions, Error Bounds and Regularization of Variational Inequalities: Part 1

Speaker’s Name: Professor Joydeep Dutta

Speaker’s Institution: Department of Mathematics and Statistics, Indian Institute of Technology Kanpur

Using gap functions to devise error bounds for some special classes of monotone variational inequality is a fruitful venture since it allows us to obtain error bounds for certain classes of convex optimization problem. It is to be noted that if we take a Hoffman type approach to obtain error bounds to the solution set of a convex programming problem it does not turn out to be fruitful and thus using the vehicle of variational inequality seems fundamental in this case. We begin the discussion by introducing several popular gap functions for variational inequalities like the Auslender gap function and the Fukushima’s regularized gap function and how error bounds can be created out of them. We then also spent a brief time with gap functions for variational inequalities with set-valued maps which correspond to the non-smooth convex optimization problems. We then quickly shift our focus on the creating error bounds using the dual gap function which is possibly the only convex gap function known in the literature to the best of our knowledge. In fact this gap function was never used for creating error bounds. Error bounds can be used as stopping criteria and this the dual gap function can be used to solve the variational inequality and also be used to develop a stopping criteria. We present several recent research on error bounds using the dual gap function and also provide an application to quasiconvex optimization.

Seminar Convenors: Matthew Tam

AGR Contacts: Andrew Danson, David Allingham