On the estimation of the convergence rate in the Janashia-Lagvilava spectral factorization algorithm

TL;DR

This paper analyzes the convergence rate of the Janashia-Lagvilava spectral factorization algorithm, providing estimates under the condition that the inverse of the spectral density matrix is integrable, enhancing understanding of its efficiency.

Contribution

It offers a new estimation of the convergence rate for the spectral factorization algorithm under specific spectral density conditions.

Findings

Convergence rate estimates are derived for the algorithm.

Results apply when the inverse spectral density matrix is integrable.

Improves theoretical understanding of the algorithm's efficiency.

Abstract

In the present paper, we estimate the convergence rate in the Janashia-Lagvilava spectral factorization algorithm (see Studia Mathematica, 137, 1999, 93-100) under the restriction on a spectral density matrix that its inverse is integrable.