Explicit inverse of a tridiagonal k−Toeplitz matrix

Summary We obtain explicit formulas for the entries of the inverse of a nonsingular and irreducible tridiagonal k–Toeplitz matrix A. The proof is based on results from the theory of orthogonal polynomials and it is shown that the entries of the inverse of such a matrix are given in terms of Chebyshev polynomials of the second kind. We also compute the characteristic polynomial of A which enables us to state some conditions for the existence of A^–1. Our results also extend known results for the case when the residue mod k of the order of A is equal to 0 or k–1 (Numer. Math., 10 (1967), pp. 153–161.).The work was supported by CMUC (Centro de Matemática da Universidade de Coimbra) and by Acção Integrada Luso-Espanhola E-6/03     

Perturbation bounds for the Cholesky andQR factorizations

LetA, A+E be Hermitian positive definite matrices. Suppose thatA=LL ^H andA+E=(L+G)(L+G)^H are the Cholesky factorizations ofA andA+E, respectively. In this paper lower bounds and upper bounds on |G|/|L| in terms of |E|/|A| are given. Moreover, perturbation bounds are given for the QR factorization of a complexm ×n matrixA of rankn.This research was supported by the National Science Foundation of China and the Department of Mathematics of Linköping University in Sweden.     

A perturbation bound for the generalized polar decomposition

LetA be anm×n complex matrix. A decompositionA=QH is termed ageneralized polar decomposition ofA ifQ is anm×n subunitary matrix (sometimes also called a partial isometry) andH a positive semidefinite Hermitian matrix. It was proved that a nonzero matrixA ^m×n has a unique generalized polar decompositionA=QH with the property (Q ^H )=(H), whereQ ^H denotes the conjugate transpose ofQ and (H) the column space ofH. The main result of this note is a perturbation bound forQ whenA is perturbed.     

Circulant and skew-circulant preconditioners for skew-hermitian type Toeplitz systems

We study the solutions of Toeplitz systemsA _n x=b by the preconditioned conjugate gradient method. Then ×n matrixA _n is of the forma ₀ I+H _n wherea ₀ is a real number,I is the identity matrix andH _n is a skew-Hermitian Toeplitz matrix. Such matrices often appear in solving discretized hyperbolic differential equations. The preconditioners we considered here are the circulant matrixC _n and the skew-circulant matrixS _n whereA _n =1/2(C _n +S _n ). The convergence rate of the iterative method depends on the distribution of the singular values of the matricesC ^–1 _n A_n andS ^–1 _n A _n . For Toeplitz matricesA _n with entries which are Fourier coefficients of functions in the Wiener class, we show the invertibility ofC _n andS _n and prove that the singular values ofC ^–1 _n A _n andS ^–1 _n A _n are clustered around 1 for largen. Hence, if the conjugate gradient method is applied to solve the preconditioned systems, we expect fast convergence.     

On the maximal number of solutions of a problem in linear inequalities

The problemy=Ax+c,x≧0,y≧0, (x, y)=0 is considered, where the square real matrixA and the real vectorc are the data and a solution is a pair of vectorsx, y. Under certain conditions on the matrixA there exists a solution for every vectorc, but it cannot be unique for everyc. We prove that under these conditions the maximal number of solutions is 2 ⁿ − 1.     

Eigenvalues of the Laplacian on symmetric regions

It was shown by Micheletti [11] that the eigenvalues of the Laplacian with Dirichlet boundary conditions are simple for most boundedC ³ regions. We show that this is not true if we restrict our attention to the subset of regions which are invariant under a group of symmetries. We also prove, for some groups, that generically the eigenspaces are irreducible spaces for the action of the group.Supported in part by FAPESP-Fundacão de Amparo à Pesquisa do Estado de São Paulo-São Paulo     

Linear algorithms for testing the sign stability of a matrix and for findingZ-maximum matchings in acyclic graphs

Summary Ann×n real matrixA=(a _ij ) isstable if each eigenvalue has negative real part, andsign stable (orqualitatively stable) if each matrix B with the same sign-pattern asA is stable, regardless of the magnitudes ofB's entries. Sign stability is of special interest whenA is associated with certain models from ecology or economics in which the actual magnitudes of thea _ij may be very difficult to determine. Using a characterization due to Quirk and Ruppert, and to Jeffries, an efficient algorithm is developed for testing the sign stability ofA. Its time-and-space-complexity are both 0(n ²), and whenA is properly presented that is reduced to 0(max{n, number of nonzero entries ofA}). Part of the algorithm involves maximum matchings, and that subject is treated for its own sake in two final sections.     

Deadbeat control: A special inverse eigenvalue problem

In this paper we give a numerical method to construct a rankm correctionBF (where then ×m matrixB is known and them ×n matrixF is to be found) to an ×n matrixA, in order to put all the eigenvalues ofA +BF at zero. This problem is known in the control literature as deadbeat control. Our method constructs, in a recursive manner, a unitary transformation yielding a coordinate system in which the matrixF is computed by merely solving a set of linear equations. Moreover, in this coordinate system one easily constructs the minimum norm solution to the problem. The coordinate system is related to the Krylov sequenceA ^–1 B,A ^–2 B,A ^–3 B, .... Partial results of numerical stability are also obtained.Dedicated to Professor Germund Dahlquist: on the occasion of his 60th birthday     

A direct method for the general solution of a system of linear equations

A computationally stable method for the general solution of a system of linear equations is given. The system isA ^Tx–B=0, where then-vectorx is unknown and then×q matrixA and theq-vectorB are known. It is assumed that the matrixA ^T and the augmented matrix [A ^T,B] are of the same rankm, wheremn, so that the system is consistent and solvable. Whenm<n, the method yields the minimum modulus solutionx _m and a symmetricn ×n matrixH _m of rankn–m, so thatx=x _m+H _my satisfies the system for ally, ann-vector. Whenm=n, the matrixH _m reduces to zero andx _m becomes the unique solution of the system.The method is also suitable for the solution of a determined system ofn linear equations. When then×n coefficient matrix is ill-conditioned, the method can produce a good solution, while the commonly used elimination method fails.This research was supported by the National Science Foundation, Grant No. GP-41158.     

A numerical procedure for computing the Moore-Penrose inverse

An algorithm for computing the Moore-Penrose inverse of an arbitraryn×m real matrixA is presented which uses a Gram-Schmidt like procedure to form anA-orthogonal set of vectors which span the subspace perpendicular to the kernel ofA. This one procedure will work for any value ofn andm, and for any value of rank (A).     

Note on an inverse characteristic value problem

Summary Given an arbitraryn ×n real matrixA andn real numberss ₁, ...,s _n , we study the problem of the existence of a diagonal matrixM such that the characteristic values ofA +M bes ₁, ...,s _n .     

An algorithm for determining if the inverse of a strictly diagonally dominant Stieltjes matrix is strictly ultrametric

Summary It was recently shown that the inverse of a strictly ultrametric matrix is a strictly diagonally dominant Stieltjes matrix. On the other hand, as it is well-known that the inverse of a strictly diagonally dominant Stieltjes matrix is a real symmetric matrix with nonnegative entries, it is natural to ask, conversely, if every strictly diagonally dominant Stieltjes matrix has a strictly ultrametric inverse. Examples show, however, that the converse is not true in general, i.e., there are strictly diagonally dominant Stieltjes matrices in ^n×n (for everyn3) whose inverses are not strictly ultrametric matrices. Then, the question naturally arises if one can determine which strictly diagonally dominant Stieltjes matrices, in ^n×n (n3), have inverses which are strictly ultrametric. Here, we develop an algorithm, based on graph theory, which determines if a given strictly diagonally dominant Stieltjes matrixA has a strictly ultrametric inverse, where the algorithm is applied toA and requires no computation of inverse. Moreover, if this given strictly diagonally dominant Stieltjes matrix has a strictly ultrametric inverse, our algorithm uniquely determines this inverse as a special sum of rank-one matrices.Research supported by the National Science FoundationResearch supported by the Deutsche Forschungsgemeinschaft     

Generalized diagonal dominance in connection with the accelerated overrelaxation (AOR) method

With the definition of generalized diagonal dominant matrices we improve the known results about the intervals of convergence of the (AOR) method for linear systems. We consider this problem for different kinds of matrices and we get some important results forH-matrices.Supported by Instituto Nacional de Investigação Cientifica.     

A constructive method of solving the Liapounov equation for complex matrices

Summary This paper describes a method of solving the Liapounov equation (1)HM+M ^* H=2D, M in upper Hessenberg form,D diagonal. Initialising the first row of the matrixA arbitrarily, one can find (by solving equations with one unknown) the unknown elements ofA such that (2)AM+M ^* A ^* =2F, whereA differs from a Hermitian matrix only in that its diagonal elements need not be real.F is a diagonal matrix which is uniquely determined by the first row ofA. By solving Eq. (2) for several initial values one may generate several matricesA andF (in the most unfavourable case 2n–1A's andF's are needed) and superpose them to getn linearly independent Hermitian matricesH _j andD _j respectively for whichH _j M+M ^* H _j =2D _j is valid. Then one can solve the real system to obtain the solution of Eq. (1).This work was performed under the terms of the agreement on association between the Max-Planck-Institut für Plasmaphysik and Euratom.     

The sum of unitary similarity orbits containing only special operators

Let B(H) be the algebra of bounded linear operator acting on a Hilbert space H (over the complex or real field). Characterization is given to A₁,…,A_k∈B(H) such that for any unitary operators is always in a special class S of operators such as normal operators, self-adjoint operators, unitary operators. As corollaries, characterizations are given to A∈B(H) such that complex, real or nonnegative linear combinations of operators in its unitary orbit U(A)={U^∗AU:Uunitary} always lie in S.     

Perturbation analysis of the orthogonal procrustes problem

Given two arbitrary real matricesA andB of the same size, the orthogonal Procrustes problem is to find an orthogonal matrixM such that the Frobenius norm MA – B is minimized. This paper treats the common case when the orthogonal matrixM is required to have a positive determinant. The stability of the problem is studied and supremum results for the perturbation bounds are derived.     

A graph theoretic approach to matrix inversion by partitioning

LetM be a square matrix whose entries are in some field. Our object is to find a permutation matrixP such thatPM P ^–1 is completely reduced, i.e., is partitioned in block triangular form, so that all submatrices below its diagonal are 0 and all diagonal submatrices are square and irreducible. LetA be the binary (0, 1) matrix obtained fromM by preserving the 0's ofM and replacing the nonzero entries ofM by 1's. ThenA may be regarded as the adjacency matrix of a directed graphD. CallD strongly connected orstrong if any two points ofD are mutually reachable by directed paths. Astrong component ofD is a maximal strong subgraph. Thecondensation D ^* ofD is that digraph whose points are the strong components ofD and whose lines are induced by those ofD. By known methods, we constructD ^* from the digraph,D whose adjacency matrixA was obtained from the original matrixM. LetA ^* be the adjacency matrix ofD ^*. It is easy to show that there exists a permutation matrixQ such thatQA ^* Q ^–1 is an upper triangular matrix. The determination of an appropriate permutation matrixP from this matrixQ is straightforward.This was an informal talk at the International Symposium on Matrix Computation sponsored by SIAM and held in Gatlinburg, Tennessee, April 24–28, 1961 and was an invited address at the SIAM meeting in Stillwater, Oklahoma on August 31, 1961     

Applications of shortest path algorithms to matrix scalings

Summary A symmetric scaling of a nonnegative, square matrixA is a matrixXAX ^–1, whereX is a nonsingular, nonnegative diagonal matrix. By associating a family of weighted directed graphs with the matrixA we are able to adapt the shortest path algorithms to compute an optimal scaling ofA, where we call a symmetric scalingA ofA optimal if it minimizes the maximum of the ratio of non-zero elements.Dedicated to Professor F.L. Bauer on the occasion of his 60th birthdayThe first author was partially supported by the Deutsche Forschungsgemeinschaft under grant GO 270/3, the second author by the U.S. National Science Foundation under grand MCS-8026132     

Asymptotic behaviour of nonlinear dispersive models with variable coefficients

We consider mathematical models of evolution which are conservative and include in the simplest case, an equation describing the unidirectional propagation of weakly nonlinear, dispersive long waves suffering disturbances due to the possible unevennes of the botton surface. Our main result gives rates of decay of the amplitude in terms of the «alterations» of the bottom surface.

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Resumo Consideramos modelos matemáticos de evolução do tipo conservativo sendo que um deles descreve a propagação unidirectional de ondas (fracamente) não- lineares e dispersivas as quais sofrem disturbios devido a possibilidade de o fundo do canal não ser raso. Nosso resultado central neste trabalho estabelece taxas de decaimento da amplitude em termos das «alteraçes» no fundo do canal.