Augmented self-concordant barriers and nonlinear optimization problems with finite complexity
In this paper we study special barrier functions for convex cones, which are the sum of a self-concordant barrier for the cone and a positive-semidefinite quadratic form. We show that the central path of these augmented barrier functions can be traced with linear speed. We also study the complexity of finding the analytic center of the augmented barrier, a problem that has some interesting applications. We show that for some special classes of quadratic forms and some convex cones, the computation of the analytic center requires an amount of operations independent of the particular data set. We argue that these problems form a class that is endowed with a property which we call finite polynomial complexity.
nonlinear optimization augmented barrier self-concordant functions finite methods