The paper presents concrete realizations of quasi-Newton methods for solving several standard problems including complementarity problems, special variational inequality problems, and the Karush–Kuhn–Tucker (KKT) system of nonlinear programming. A new approximation idea is introduced in this paper. The Q-superlinear convergence of the Newton method and the quasiNewton method are established und...

In this paper we follow up our discussion on algorithms suitable for optimization of systems governed by partial differential equations. In the first part of of this paper we proposed a Lagrange-Newton-Krylov-Schur method (LNKS) that uses Krylov iterations to solve the Karush-Kuhn-Tucker system of optimality conditions, but invokes a preconditioner inspired by reduced space quasi-Newton algorit...

We propose a path-following version of the Todd-Burrell procedure to solve linear programming problems with an unknown optimal value. The path-following scheme is not restricted to Karmarkar's primal step; it can also be implemented with a dual Newton step or with a primal-dual step.

Convergence properties are presented for Newton additive and multiplicative Schwarz iterative methods for the solution of nonlinear systems in several variables. These methods consist of approximate solutions of the linear Newton step using either additive or multiplicative Schwarz iterations, where overlap between subdomains can be used. Restricted versions of these methods are also considered...

The Hermitian and skew-Hermitian splitting (HSS) method is an unconditionally convergent iteration method for solving large sparse non-Hermitian positive definite system of linear equations. By making use of the HSS iteration as the inner solver for the Newton method, we establish a class of Newton-HSS methods for solving large sparse systems of nonlinear equations with positive definite Jacobi...

We study the smooth structure of convex functions by generalizing a powerful concept so-called self-concordance introduced by Nesterov and Nemirovskii in the early 1990s to a broader class of convex functions, which we call generalized self-concordant functions. This notion allows us to develop a unified framework for designing Newton-type methods to solve convex optimization problems. The prop...