José Domingo Jiménez-López

arXiv2026

$αβ$-Tessarine Toolbox: A high-performance MATLAB framework for hypercomplex tensor algebra

The \textit{$αβ$-Tessarine Toolbox} is introduced as a high-performance MATLAB framework for generalized hypercomplex tensor algebra. By implementing the $αβ$-tessarine mathematical structure, a strictly more general environment than existing quaternion or limited tessarine packages is provided. This open-source software bridges abstract theoretical models and experimental applications, allowing researchers to efficiently solve multidimensional problems. Built-in capabilities include exact hypercomplex matrix factorizations, such as Singular Value Decomposition (SVD) and QR, optimized for large-scale image in-painting, denoising, and digital watermarking. The scalable architecture facilitates new computational research in machine learning and multidimensional signal processing.

math.AC↗

arXiv2025

Advancing Computational Tools for Analyzing Commutative Hypercomplex Algebras

Commutative hypercomplex algebras offer significant advantages over traditional quaternions due to their compatibility with linear algebra techniques and efficient computational implementation, which is crucial for broad applicability. This paper explores a novel family of commutative hypercomplex algebras, referred to as (alpha,beta)-tessarines, which extend the system of generalized Segre's quaternions and, consequently, elliptic quaternions. The main contribution of this work is the development of theoretical and computational tools for matrices within this algebraic system, including inversion, square root computation, LU factorization with partial pivoting, and determinant calculation. Additionally, a spectral theory for (alpha,beta)-tessarines is established, covering eigenvalue and eigenvector analysis, the power method, singular value decomposition, rank-k approximation, and the pseudoinverse. Solutions to the classical least squares problem are also presented. These results not only enhance the fundamental understanding of hypercomplex algebras but also provide researchers with novel matrix operations that have not been extensively explored in previous studies. The theoretical findings are supported by real-world examples, including image reconstruction and color face recognition, which demonstrate the potential of the proposed techniques.

math.RA↗