نتایج جستجو برای: joint rank k numerical range

تعداد نتایج: 1533066  

2009
Vladimı́r Müller

Abstract. Let T1, . . . , Tn be bounded linear operators on a complex Hilbert space H. Then there are compact operators K1, . . . , Kn ∈ B(H) such that the closure of the joint numerical range of the n-tuple (T1−K1, . . . , Tn−Kn) equals to the joint essential numerical range of (T1, . . . , Tn). This generalizes the corresponding result for n = 1. We also show that if S ∈ B(H) and n ∈ N then t...

Journal: :Applied Mathematics Letters 2001

Journal: :Studia Mathematica 2022

It is shown that for $n \le 3$ the joint numerical range of a family commuting $n\times n$ complex matrices always convex; \ge 4$ there are two whose not convex.

Journal: :Linear Algebra and its Applications 2010

2007
YUAN WU

For any n-by-n complex matrix A and any k, 1 ≤ k ≤ n, let Λk(A) = {λ ∈ C : X∗AX = λIk for some n-by-k X satisfying X∗X = Ik} be its rank-k numerical range. It is shown that if A is an n-by-n contraction, then Λk(A) = ∩{Λk(U) : U is an (n + dA)-by-(n + dA) unitary dilation of A}, where dA = rank (In − A∗A). This extends and refines previous results of Choi and Li on constrained unitary dilations...

Journal: :SIAM J. Matrix Analysis Applications 2000
Chi-Kwong Li Yiu-Tung Poon

We consider linearly independent families of Hermitian matrices {A1, . . . , Am} so thatWk(A) is convex. It is shown that m can reach the upper bound 2k(n− k) + 1. A key idea in our study is relating the convexity of Wk(A) to the problem of constructing rank k orthogonal projections under linear constraints determined by A. The techniques are extended to study the convexity of other generalized...

‎Let $n$ and $k$ be two positive integers‎, ‎$kleq n$ and $A$ be an $n$-square quaternion matrix‎. ‎In this paper‎, ‎some results on the $k-$numerical range of $A$ are investigated‎. ‎Moreover‎, ‎the notions of $k$-numerical radius‎, ‎right $k$-spectral radius and $k$-norm of $A$ are introduced‎, ‎and some of their algebraic properties are studied‎.

Journal: :SIAM Journal on Matrix Analysis and Applications 2022

The canonical polyadic decomposition (CPD) of a low-rank tensor plays major role in data analysis and signal processing by allowing for unique recovery underlying factors. However, it is well known that the CPD approximation problem ill-posed. That is, may fail to have best rank $R$ when $R>1$. This article gives deterministic bounds existence approximations over ${\mathbb{K}}={\mathbb{R}}$ or ...

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