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.     

Circularity of numerical ranges and block-shift matrices

Let A be a square complex matrix. Several characterizations are found for A to be permutationally similar to a block-shift matrix. One interesting equivalent condition is that the numerical range of every matrix with the same zero pattern as A is a circular disk. Equivalent conditions for the characteristic polynomial of the hermitian part of the matrix e^iθA to be the same for all real values θ are also obtained.Complex matrices of order four that are unitarily similar to a block-shift matrix are identified. A result of Marcus and Pesce [6] is extended, and an open question of Li and Tsing [4] is also answered partially.     

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.     

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.     

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.     

Operator trigonometry

The purpose of the present paper is to survey, from a historical perspective and including some new results, a theory which I will call operator trigonometry. This theory, which is little known, is closely associated with the numerical range W(A) of an operator A. Among the new results is a beautiful connection to numerical linear algebra, in which gradient descent and conjugate gradient convergence rates are shown to be trigonometric.     

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.     

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.     

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.     

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.     

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.     

Feasible strategies for the control of a disk rolling on a moving horizontal plane

Let (X,Y,Z) be an inertial coordinate system and suppose that a horizontal plane is moving in a uniform velocity parallel to the (X,Y)-plane. A disk is rolling on the moving plane. Given two points A and B fixed in the (X,Y)-plane. Open-loop strategies are computed, for rolling the disk, on the moving plane, from A to B, during a given time interval [0, _f] and subject to state and control constraints.     

A stable range for quadratic forms over commutative rings

A commutative ring A has quadratic stable range 1 (qsr(A) = 1) if each primitive binary quadratic form over A represents a unit. It is shown that qsr(A) = 1 implies that every primitive quadratic form over A represents a unit, A has stable range 1 and finitely generated constant rank projectives over A are free. A classification of quadratic forms is provided over Bezout domains with characteristic other than 2, quadratic stable range 1, and a strong approximation property for a certain subset of their maximum spectrum. These domains include rings of holomorphic functions on connected noncompact Riemann surfaces. Examples of localizations of rings of algebraic integers are provided to show that the classical concept of stable range does not behave well in either direction under finite integral extensions and that qsr(A) = 1 does not descend from such extensions.     

On the derivative of the Perron vector whose infinity norm is fixed

In the recent paper the authors studied the derivaties of the Perron vector at an n × n essentially nonnegative and irreducible matrix A when the Perron vector is subjected to the normalization that one of its components is held a fixed constant in a neighbourhood of A or that the pth norm of the of the eigenvector is held a fixed constant in such a neighborhood. The Perron vector subject to the normalization that its infinity norm is held a fixed constant in a neighborhood of A does not necessarily imply that it is differentiable at A. In this paper we give formulas for the first derivative of this Perron vector where it is differentiable. Our formulas also accommodate left and right derivatives of the eigenvector.     

A note on sign-solvability of linear system of equations

Given an n×(n+I) sign matrix (Ab). we are concerned with the condition that guarantees the solution to Ax=b be of a fixded sign pattern. It is shown that each factor of det (1) corresponds. in aone to one way to DM-component of the bipartite graph representation of A. This fact is used to supplement the proof of the graph-theoretic characterization of sign-solvability obtained by Bassett et al. [1] and subsequent authors.     

Complementarity forms of theorems of Lyapunov and Stein, and related results

The well-known Lyapunov's theorem in matrix theory / continuous dynamical systems asserts that a (complex) square matrix A is positive stable (i.e., all eigenvalues lie in the open right-half plane) if and only if there exists a positive definite matrix X such that AX+XA^* is positive definite. In this paper, we prove a complementarity form of this theorem: A is positive stable if and only if for any Hermitian matrix Q, there exists a positive semidefinite matrix X such that AX+XA^*+Q is positive semidefinite and X[AX+XA^*+Q]=0. By considering cone complementarity problems corresponding to linear transformations of the form I−S, we show that a (complex) matrix A has all eigenvalues in the open unit disk of the complex plane if and only if for every Hermitian matrix Q, there exists a positive semidefinite matrix X such that X−AXA^*+Q is positive semidefinite and X[X−AXA^*+Q]=0. By specializing Q (to −I), we deduce the well known Stein's theorem in discrete linear dynamical systems: A has all eigenvalues in the open unit disk if and only if there exists a positive definite matrix X such that X−AXA^* is positive definite.     

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.     

Range invariance of certain matrix products

A necessarv and sufficient condition is established for the product AB C to have its range.A (AB C), invariant with respect to the choice of a generalized inverse B .This result is then used to derive criteria for the invariance of the subspaces A(AB ).A(B C)A(B) and A(BB C) and also to deduce that the simultaneous invariance of the range of AB^- C and the range of its conjugate transpose entails the invariance of the product AB^-C itself.     

Extremal patterns of distinct entries in vectors in the range of a matrix

For an m × n matrix A over a field F we consider the following quantities: μ(A), the maximum multiplicity of a field element as a component of a nonzero vector in the range of A, and δ(A), the minimum number of distinct entries in a nonzero vector in the range of A. In terms of ramk(A), we describe the set of possible values of μand δ and discuss the possible relations between them. We also develop a general affine geometric structure in which the sets of values of μ and δ may be characterized linear algebraically.     

On generalized Boolean functions II

This cycle of papers is based on the concept of generalized Bolean functions introduced by the author in the first article of the series. Every generalized Boolean function f:Bⁿ→B can be written in a manner similar to the canonical disjunctive form using some function defined on A×B, where A is a finite subset of B containing 0 and 1. The set of those functions f is denoted by GBF_n[A]. In this paper the following questions are presented: (1) What is the relationship between GBF_n[A₁] and GBF_n[A₂] when A₁A₂. (2) What can be said about GBF_n[A₁∩A₂] and GBF_n[A₁A₂] in comparison with GBF_n[A₁]∩GBF_n[A₂] and GBF_n[A₁]GBF_n[A₂], respectively.