Methods and algorithms of solving spectral problems for polynomial and rational matrices

This paper continues the series of publications devoted to surveying and developing methods for solving the following problems for a two-parameter matrix F (λ, μ) of general form: exhausting points of the mixed regular spectrum of F (λ, μ); performing operations on polynomials in two variables (computing the GCD and LCM of a few polynomials, division of polynomials, and factorization); computing a minimal basis of the null-space of polynomial solutions of the matrix F (λ, μ) and separation of its regular kernel; inversion and pseudo in version of polynomial and rational matrices in two variables, and solution of systems of nonlinear algebraic equations in two unknowns. Most of the methods suggested are based on rank factorizations of a two-parameter polynomial matrix and on the method of hereditary pencils. Bibliography: 8 titles.

A new approach for dealing with the structural properties of polynomial and rational matrices at infinity is presented. In this first part of the development, the attention is focused on polynomial matrices. Some fundamental notions and definitions such as the notion of homogeneous form for polynomial matrices are introduced. These enable us to define important concepts such as the cancellation at infinity, coprimeness at infinity, etc. for polynomial matrices in precisely the same way as for their finite counterparts. We demonstrate that our approach allows interesting generalizations of some important conventional concepts.

In this paper, rank factorizations and factor left prime factorizations are studied. The authors prove that any polynomial matrix with full row rank has factor left prime factorizations. And for a class of polynomial matrices, the authors give an algorithm to decide whether they have rank factorizations or factor left prime factorizations and compute these factorizations if they exist.

This note presents a numerical method of computing a coprime fraction of a two-dimensional (2-D) rational matrix, not necessarily proper. It is achieved by searching the primary linearly dependent rows, in order from top to bottom, of the two generalized resultants. The procedure can be extended to the three- or higher dimensional case and the result can also be used to compute the greatest common divisor (GCD) of 2-D polynomial matrices without employing primitive factorizations which does not exist in the three- or higher dimensional case.

The protection of sensitive and confidential data become a challenging task in the present scenario as more and more digital data is stored and transmitted between the end users. The privacy is vitally necessary in case of medical data, which contains the important information of the patients. In this article, a novel biometric inspired medical encryption technique is proposed based on newly introduced parameterized all phase orthogonal transformation (PR-APBST), singular value, and QR decomposition. The proposed technique utilizes the biometrics of the patient/owner to generate a key management system to obtain the parameters involved in the proposed technique. The medical image is then encrypted employing PR-APBST, QR and singular value decomposition and is ready for secure transmission or storage. Finally, a reliable decryption process is employed to reconstruct the original medical image from the encrypted image. The validity and feasibility of the proposed framework have been demonstrated using an extensive experiments on various medical images and security analysis.

Observability and Controllability of General Linear Systems treats five different families of the linear systems, three of which are new. The book begins with the definition of time together with a brief description of its crucial properties. It presents further new results on matrices, on polynomial matrices, on matrix polynomials, on rational matrices, and on the new compact, simple and elegant calculus that enabled the generalization of the transfer function matrix concept and of the state concept, the proofs of the new necessary and sufficient observability and controllability conditions for all five classes of the studied systems. Features • Generalizes the state space concept and the complex domain fundamentals of the control systems unknown in previously published books by other authors. • Addresses the knowledge and ability necessary to overcome the crucial lacunae of the existing control theory and drawbacks of its applications. • Outlines new effective mathematical means for effective complete analysis and synthesis of the control systems. • Upgrades, completes and broadens the control theory related to the classical self-contained control concepts: observability and controllability. • Provides information necessary to create and teach advanced inherently upgraded control courses.

11.15 which describe a certain simple dynamical model. Suppose that the initial conditions for 01, 81, 02, 82 are all zero. Applying the Laplace transform to these equations then produces The 2 x 2 matrix A(s) on the left in (12.2) is an example of a polynomial matrix, whose elements are themselves polynomials. As for a scalar polynomial, A(s) can be written in terms of powers of s:

This paper addresses an algorithm of J spectral factorization for discrete time polynomial matrices. Particularly, we focus on the computation of J spectral factorization for discrete time polynomial matrices. Our approach is based on two-variable polynomial matrices, which is introduced for the analysis and synthesis of discrete time dissipativeness by authors, and algebraic Riccati equations. We show that the solvablity of J spectral factorization of a given polynomial matrix is equivalent to that of an algebraic Riccati equation consisting of the coefficient matrices of the given polynomial matrix. And then we provide an algorithm of discrete time J spectral factorization. Moreover, we give an illustrative example in order to show the validity of our results.

Chapter 9 - Numerical Techniques

New remarks on the factorization and equivalence problems for a class of multivariate polynomial matrices

The algorithm of ∇V-factorization, suggested earlier for decomposing one- and two-parameter polynomial matrices of full row rank into a product of two matrices (a regular one, whose spectrum coincides with the finite regular spectrum of the original matrix, and a matrix of full row rank, whose singular spectrum coincides with the singular spectrum of the original matrix, whereas the regular spectrum is empty), is extended to the case of q-parameter (q ≥ 1) polynomial matrices. The algorithm of ∇V-q factorization is described, and its justification and properties for matrices with arbitrary number of parameters are presented. Applications of the algorithm to computing irreducible factorizations of q-parameter matrices, to determining a free basis of the null-space of polynomial solutions of a matrix, and to finding matrix divisors corresponding to divisors of its characteristic polynomial are considered. Bibliogrhaphy: 4 titles.

Spectral problems for multiparameter polynomial matrices are considered. The notions of the spectrum (including those of its finite, infinite, regular, and singular parts), of the analytic multiplicity of a point of the spectrum, of bases of null-spaces, of Jordan s-semilattices of vectors and of generating vectors, and of the geometric and complete geometric multiplicities of a point of the spectrum are introduced. The properties of the above characteristics are described. A method for linearizing a polynomial matrix (with respect to one or several parameters) by passing to the accompanying pencils is suggested. The interrelations between spectral characteristics of a polynomial matrix and those of the accompanying pencils are established. Bibliography: 12 titles.

This paper is an updated and extended version of the paper "The QR Decomposition and the Singular Value Decomposition in the Symmetrized Max-Plus Algebra" (B. De Schutter and B. De Moor, SIAM J. Matrix Anal. Appl., 19 (1998), pp. 378--406). The max-plus algebra, which has maximization and addition as its basic operations, can be used to describe and analyze certain classes of discrete-event systems, such as flexible manufacturing systems, railway networks, and parallel processor systems. In contrast to conventional algebra and conventional (linear) system theory, the max-plus algebra and the max-plus-algebraic system theory for discrete-event systems are at present far from fully developed, and many fundamental problems still have to be solved. Currently, much research is going on to deal with these problems and to further extend the max-plus algebra and to develop a complete max-plus-algebraic system theory for discrete-event systems. In this paper we address one of the remaining gaps in the max-plus algebra by considering matrix decompositions in the symmetrized max-plus algebra. The symmetrized max-plus algebra is an extension of the max-plus algebra obtained by introducing a max-plus-algebraic analogue of the $-$-operator. We show that we can use well-known linear algebra algorithms to prove the existence of max-plus-algebraic analogues of basic matrix decompositions from linear algebra such as the QR decomposition, the singular value decomposition, the Hessenberg decomposition, the LU decomposition, and so on. These max-plus-algebraic matrix decompositions could play an important role in the max-plus-algebraic system theory for discrete-event systems.

An algorithm for inverting rational matrices

Methods for computing scalar and vector spectral characteristics of a polynomial matrix are proposed. These methods are based on determining the so-called generating vectors (eigenvectors and principal vectors) by using the method of rank factorization of polynomial matrices. The possibility of extending the methods to the case of two-parameter polynomial matrices is indicated. Bibliography: 4 titles.