Interior point methods for optimization have been around for more than 25 years now. Their
presence has shaken up the field of optimization. Interior point methods for linear and …

In this paper we will discuss two variants of an inexact feasible interior point algorithm for
convex quadratic programming. We will consider two different neighborhoods: a small one …

Large-scale optimization problems that seek sparse solutions have become ubiquitous.
They are routinely solved with various specialized first-order methods. Although such …

The solution of KKT systems is ubiquitous in optimization methods and often dominates the
computation time, especially when large-scale problems are considered. Thus, the effective …

When an iterative method is applied to solve the linear equation system in interior point
methods (IPMs), the attention is usually placed on accelerating their convergence by …

This work focuses on the iterative solution of sequences of KKT linear systems arising in
interior point methods applied to large convex quadratic programming problems. This task is …

This paper introduces a framework for implementing log-domain interior-point methods
(LDIPMs) using inexact Newton steps. A generalized inexact iteration scheme is established …

We focus on efficient preconditioning techniques for sequences of Karush‐Kuhn‐Tucker
(KKT) linear systems arising from the interior point (IP) solution of large convex quadratic …

Abstract In the Cultural Heritage domain, novel fruition and enjoyment approaches, based
on Internet of Things (IoT) paradigm, have the effect to change the way people experiencing …

Supervised classification is one of the most powerful techniques to analyze data, when a-
priori information is available on the membership of data samples to classes. Since the …