Equality of higher numerical ranges of matrices and a conjecture of Kippenhahn on Hermitian pencils

Let M_n be the algebra of all n × n complex matrices. For 1 k n, the kth numerical range of A M_n is defined by W_k(A) = (1/k)∑_j^k=1x_j^*Ax_j : x₁, …, x_k is an orthonormal set in ⁿ]. It is known that tr A/n = W_n(A) W_n−1(A) W₁(A). We study the condition on A under which W_m(A) = W_k(A) for some given 1 m < k n. It turns out that this study is closely related to a conjecture of Kippenhahn on Hermitian pencils. A new class of counterexamples to the conjecture is constructed, based on the theory of the numerical range.     

Computer generated numerical ranges and some resulting theorems

The numerical rangeW(A), of an arbitrary n-square matrix A is the union of the numerical ranges of all 2-square real compressions of A. As a result, a simple graphics program is written that accurately exhibits W(A) for real A, and suggests several conjectures relating the geometry of W(A) to algebraic properties of A. Some of these conjectures are analyzed in the final sections of the paper.     

Numerical ranges and dilations

Denote by W(A) the numerical range of a bounded linear operator A. For two operators A and B (which may act on different Hilbert spaces), we study the relation between the inclusion relation W(A)⊆W(B) and the condition that A can be dilated to an operator of the form B⊗I. We also investigate the possibilities of dilating an operator A to operators with simple structure under the assumption that W(A) is included in a special region.     

Note on a paper of thompson: the congruence numerical range

Given n×n Complex matrices A, Cdefine the C-congruence numerical range of A to be the set [ILM0001]. R.C. Thompson has characterized R_C(A) when [ILM0002] are fixed complex numbers. In this note. we obtain some analogous results about R_t(A) when C is skew symmmetric and a simple proof of the result of Thompson is given.Moreover, we characterize a certain set of partial off diagonals under congruence unitary transformation.     

The numerical range of certain 0,1-matrices

Let A be a 0, 1-matrix with at most one 1 in each row and column. The authors prove that the numerical range of A is the convex hull of a polygon and a circular disk, both centered at the origin and contained in the unit disk. The proof uses a permutation similarity to reduce A to a direct sum of matrices whose numerical ranges can be determined. A computer program, developed by the authors, which plots the boundary of the numerical range of an arbitrary complex matrix is also discussed.     

Quaternionic Numerical Range and Real Subspaces

Let W(A) be the numerical range of an n × n quaternionic matrix A and V a real subspace of the skew field of real quaternions. In this note the authors consider the relation among the shape of W(A), the convexity of V∩W(A): and the validity of the equality V∩W(A) = W_v(A), where W_v (A) is the orthogonal projection of W(A) into V.     

Alternative proofs of a theorem of Moyls and Marcus on the numerical range of a square matrix

Moyls and Marcus [4] showed that for n≤4,n×n an complex matrix A is normal if and only if the numerical range of A is the convex hull of the eigenvalues of A. When n≥5, there exist matrices which are not normal, but such that the numerical range is still the convex hull of the eigenvalues. Two alternative proofs of this fact are given. One proof uses the known structure of the numerical range of a 2×2 matrix. The other relies on a theorem of Motzkin and Taussky stating that a pair of Hermitian matrices with property L must commute.     

The polynomial numerical hull of a matrix and algorithms for computing the numerical range

Let A be an n × n matrix. In this paper we discuss theoretical properties of the polynomial numerical hull of A of degree one and assemble them into three algorithms to computing the numerical range of A.     

Combinatorial bases, derived jordan sets and the equality of the height and level characteristics of an M-matrix

We continue our series of papers on the graph theoretic spectral theory of matrices. Let A be an M-matrix. We introduce the concepts of combinatorial vectors and proper combinatorial vectors in the generalized nullspace E(A) of A. We explore the properties of combinatorial bases for E(A) and Jordan bases for E(A) derived from proper combinatorial sets of vectors. We use properties of these bases to prove additional new conditions for the equality of the (spectral) height (or Weyr) characteristic and the (graph theoretic) level characteristic of A. We and DMS-also explore the role of the Hall Marriage Condition, well structured graphs and their anchored chain decompositions in the study of the equality of the two characteristics.     

Approximate inverse preconditioner by computing approximate solution of Sylvester equation

This paper presents a new method for obtaining a matrix M which is an approximate inverse preconditioner for a given matrix A, where the eigenvalues of A all either have negative real parts or all have positive real parts. This method is based on the approximate solution of the special Sylvester equation AX + XA = 2I. We use a Krylov subspace method for obtaining an approximate solution of this Sylvester matrix equation which is based on the Arnoldi algorithm and on an integral formula. The computation of the preconditioner can be carried out in parallel and its implementation requires only the solution of very simple and small Sylvester equations. The sparsity of the preconditioner is preserved by using a proper dropping strategy. Some numerical experiments on test matrices from Harwell–Boing collection for comparing the numerical performance of the new method with an available well-known algorithm are presented.     

Convexoid and generalized derivations

Let E,F be two Banach spaces and let S be a symmetric norm ideal of L(E,F). For AL(F) and BL(E) the generalized derivation δ_S,A,B is the operator on S that sends X to AX−XB. A bounded linear operator is said to be convexoid if its (algebraic) numerical range coincides with the convex hull of its spectrum. We show that δ_S,A,B is convexoid if and only if A and B are convexoid.     

Some results on symplectic matrices

The principal results are that if A is an integral matrix such that AA^T is symplectic then A = CQ, where Q is a permutation matrix and C is symplectic; and that if A is a hermitian positive definite matrix which is symplectic, and B is the unique hermitian positive definite pth.root of A, where p is a positive integer, then B is also symplectic.     

Structural and computational properties of possibly singular semiseparable matrices

A classical result of structured numerical linear algebra states that the inverse of a nonsingular semiseparable matrix is a tridiagonal matrix. Such a property of a semiseparable matrix has been proved to be useful for devising linear complexity solvers, for establishing recurrence relations among its columns or rows and, moreover, for efficiently evaluating its characteristic polynomial. In this paper, we provide sparse structured representations of a semiseparable matrix A which hold independently of the fact that A is singular or not. These relations are found by pointing out the band structure of the inverse of the sum of A plus a certain sparse perturbation of minimal rank. Further, they can be used to determine in a computationally efficient way both a reflexive generalized inverse of A and its characteristic polynomial.     

The robertson-taussky inequality revisited

Let a positive definite Hermitian matrix HεMⁿ(C) be decomposed as H=A + iB, with A, B ε M_nm(R). We give two new proofs of the inequality det H ≤ det A (with equality iff B = 0. each of which vields something futher. One exhibits majorization between the eigenvalues of A and H the other allows proof of the permanental analog per H≥per A.     

The solution of linear systems by using the Sherman–Morrison formula

We consider the problem of solving the linear system Ax = b, where A is the coefficient matrix, b is the known right hand side vector and x is the solution vector to be determined. Let us suppose that A is a nonsingular square matrix, so that the linear system Ax = b is uniquely solvable.

The well known Sherman–Morrison formula, that gives the inverse of a rank-one perturbation of a matrix from the knowledge of the unperturbed inverse matrix, is used to compute the numerical solution of arbitrary linear systems, in fact it can be repetitively applied to invert an arbitrary matrix. We describe some interesting properties of the method proposed.

Finally we show some numerical results obtained with the method proposed.     

Topological invariants in forced piecewise-linear FitzHugh–Nagumo-like systems

Mathematical models for periodically-forced excitable systems arise in many biological and physiological contexts. Chaotic dynamics of a forced piecewise-linear Fitzhugh–Nagumo-like system under large-amplitude forcing was identified by Othmer and Xie in their work [J. Math. Biol. 39 (1999) 139]. Using kneading theory we study the topological entropy of some chaotic return maps associated with a singular system. Finally we introduce a new topological invariant to distinguish isentropic dynamics and we exhibit numerical results about maps with the same topological entropy, that suggest the existence of a relation between the parameters A and θ, when T is fixed.     

Flat portions on the boundary of the numerical ranges of certain Toeplitz matrices

We give criterions for a flat portion to exist on the boundary of the numerical range of a matrix. A special type of Teoplitz matrices with flat portions on the boundary of its numerical range are constructed. We show that there exist 2 × 2 nilpotent matrices A₁,A₂, an n  × n nilpotent Toeplitz matrix N_n, and an n  × n cyclic permutation matrix S_n(s) such that the numbers of flat portions on the boundaries of W(A₁⊕N_n) and W(A₂⊕S_n(s)) are, respectively, 2(n - 2) and 2n.     

Linear operators preserving unitarily invariant norms of matrices

Let F_{m × n} be the set of all m × n matrices over the field F = C or R Denote by U_n(F) the group of all n × n unitary or orthogonal matrices according as F = C or F-R. A norm N() on Fm ×n, is unitarily invariant if N(UAV) = N(A): for all A ∈ F _m×n U ∈ U_m(F). and V ∈ U_n(F). We characterize those linear operators TF_{m × n} → F_{m × n}which satisfy N (T(A)) = N(A)for all A ∈ F_{m × n}

for a given unitarily invariant norm N(). It is shown that the problem is equivalent to characterizing those operators which preserve certain subsets in F_{m × n} To develop the theory we prove some results concerning unitary operators on F_{m × n} which are of independent interest.     

Numerical approximation of the product of the square root of a matrix with a vector

Given an n×n symmetric positive definite matrix A and a vector , two numerical methods for approximating are developed, analyzed, and computationally tested. The first method applies a Newton iteration to a specific nonlinear system to approximate while the second method applies a step-control method to numerically solve a specific initial-value problem to approximate . Assuming that A is first reduced to tridiagonal form, the first method requires O(n²) operations per iteration while the second method requires O(n) operations per iteration. In contrast, numerical methods that first approximate A^1/2 and then compute generally require O(n³) operations per iteration.