Abstract: For parahermitian polynomial matrices, which can be used, for example, to characterize space-time covariance in broadband array processing, the conventional eigenvalue decomposition (EVD) ...

Abstract: Matrix diagonalization (or termed, matrix eigen-decomposition) is a vital part of matrix theory. Different from static matrix diagonalization problem, in this paper, the more challenging ...

Department of Cyber-Physical Systems, Clark Atlanta University, Atlanta, GA, USA. where P is some reflection (symmetric signed permutation) matrix. Like U, the generalized reflexive matrices A arise ...

This is a simple C++ program for computing the eigenvalues and eigenvectors of any square matrix using the LAPACK library. Diagonalization is handled by the "diagonalization" class, which is defined ...

Joint approximate diagonalization (JAD) of multiple matrices is a core problem in many applications. In this work we propose two numerical methods for computing JAD, based on constrained optimization ...

The self-consistent solution of a Schrödinger-like equation for the density matrix is a critical and computationally demanding step in quantum-based models of interatomic bonding. This step was ...

ABSTRACT: A modal identification algorithm is developed, combining techniques from Second Order Blind Source Separation (SOBSS) and State Space Realization (SSR) theory. In this hybrid algorithm, a ...

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When taking the matrix exponential of an Hermitean matrix M (for example to compute the time evolution operator from the Hamiltonian of a closed system) it is sometimes faster to first diagonalize the ...