Optimal diagonal preconditioning beyond worst-case conditioning: theory and practice of omega scaling

Preconditioning is essential in many areas of mathematics, and in particular is a fundamental tool for accelerating iterative methods for solving linear systems. In this work, we study optimal diagonal preconditioning under two distinct notions of conditioning: the classical worst-case \(\kappa\)-condition number and the averaging-based \(\omega\)-condition number. We observe that \(\omega\)-optimal preconditioning generally outperforms \(\kappa\)-optimal … Read more

Approaches to iterative algorithms for solving nonlinear equations with an application in tomographic absorption spectroscopy

In this paper we propose an approach for solving systems of nonlinear equations without computing function derivatives. Motivated by the application area of tomographic absorption spectroscopy, which is a highly-nonlinear problem with variables coupling, we consider a situation where straightforward translation to a fixed point problem is not possible because the operators that represent the … Read more

Preconditioning for Generelized Jacobians with the ω-Condition Number

Preconditioning is essential in iterative methods for solving linear systems of equations. We study a nonclassic matrix condition number, the ω-condition number, in the context of optimal conditioning for low rank updating of positive definite matrices. For a positive definite matrix, this condition measure is the ratio of the arithmetic and geometric means of the … Read more

The superiorization method with restarted perturbations for split minimization problems with an application to radiotherapy treatment planning

In this paper we study the split minimization problem that consists of two constrained minimization problems in two separate spaces that are connected via a linear operator that maps one space into the other. To handle the data of such a problem we develop a superiorization approach that can reach a feasible point with reduced … Read more