Characterizing matrices with eigenvalues in an LMI region: A dissipative-Hamiltonian approach

10/13/2022
by   Neelam Choudhary, et al.
0

In this paper, we provide a dissipative Hamiltonian (DH) characterization for the set of matrices whose eigenvalues belong to a given LMI region. This characterization is a generalization of that of Choudhary et al. (Numer. Linear Algebra Appl., 2020) to any LMI region. It can be used in various contexts, which we illustrate on the nearest Ω-stable matrix problem: given an LMI region Ω⊆ℂ and a matrix A ∈ℂ^n,n, find the nearest matrix to A whose eigenvalues belong to Ω. Finally, we generalize our characterization to more general regions that can be expressed using LMIs involving complex matrices.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/05/2022

Solving matrix nearness problems via Hamiltonian systems, matrix factorization, and optimization

In these lectures notes, we review our recent works addressing various p...
research
08/24/2020

Eigenvalues and Eigenvectors of Tau Matrices with Applications to Markov Processes and Economics

In the context of matrix displacement decomposition, Bozzo and Di Fiore ...
research
03/13/2023

Distance Evaluation to the Set of Defective Matrices

We treat the problem of the Frobenius distance evaluation from a given m...
research
02/17/2020

Nearest Ω-stable matrix via Riemannian optimization

We study the problem of finding the nearest Ω-stable matrix to a certain...
research
01/24/2020

Distance problems for dissipative Hamiltonian systems and related matrix polynomials

We study the characterization of several distance problems for linear di...
research
11/14/2019

An Invariant Test for Equality of Two Large Scale Covariance Matrices

In this work, we are motivated by the recent work of Zhang et al. (2019)...
research
06/16/2023

Matrix Diagonalization as a Board Game: Teaching an Eigensolver the Fastest Path to Solution

Matrix diagonalization is at the cornerstone of numerous fields of scien...

Please sign up or login with your details

Forgot password? Click here to reset