In this talk we will discuss similarities between two homotopy-based approaches:
- (inexact) primal-dual interior point method for LP/QP, and
- preconditioned Newton conjugate gradient method for big data optimization.
Both approaches rely on clever exploitation of the curvature of optimized functions and deliver efficient techniques for solving optimization problems of unprecedented sizes. We will address both theoretical and practical aspects of these methods.