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Related papers: Spectral Sets: Numerical Range and Beyond

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The numerical range in the quaternionic setting is, in general, a non convex subset of the quaternions. The essential numerical range is a refinement of the numerical range that only keeps the elements that have, in a certain sense,…

Functional Analysis · Mathematics 2022-10-12 Luís Carvalho , Cristina Diogo , Sérgio Mendes , Helena Soares

We show that the spectral norm of a random $n_1\times n_2\times \cdots \times n_K$ tensor (or higher-order array) scales as $O\left(\sqrt{(\sum_{k=1}^{K}n_k)\log(K)}\right)$ under some sub-Gaussian assumption on the entries. The proof is…

Statistics Theory · Mathematics 2014-07-09 Ryota Tomioka , Taiji Suzuki

Several new verifiable conditions are established for block matrices with scalar diagonal blocks to have the numerical range equal the convex hull of at most k ellipses where k by k is the size of the smaller diagonal block. For k = 2,…

Functional Analysis · Mathematics 2020-02-25 Titas Geryba , Ilya M. Spitkovsky

We consider smoothed versions of geometric range spaces, so an element of the ground set (e.g. a point) can be contained in a range with a non-binary value in $[0,1]$. Similar notions have been considered for kernels; we extend them to more…

Computational Geometry · Computer Science 2015-11-02 Jeff M. Phillips , Yan Zheng

Kippenhahn discovered that the numerical range of a complex square matrix is the convex hull of a plane real algebraic curve. Here, we present an example of a convex set, which has a similar algebraic description as the numerical range,…

Functional Analysis · Mathematics 2023-09-01 Stephan Weis

The aim of this paper is to describe the closure of the numerical range of the product of two orthogonal projections in Hilbert space as a closed convex hull of some explicit ellipses parametrized by points in the spectrum. Several…

Functional Analysis · Mathematics 2013-09-23 Hubert Klaja

We analyze the numerical range of high-dimensional random matrices, obtaining limit results and corresponding quantitative estimates in the non-limit case. For a large class of random matrices their numerical range is shown to converge to a…

Operator Algebras · Mathematics 2014-05-13 Benoît Collins , Piotr Gawron , Alexander E. Litvak , Karol Życzkowski

The numerical radius of a matrix is a scalar quantity that has many applications in the study of matrix analysis. Due to the difficulty in computing the numerical radius, inequalities bounding it have received a considerable attention in…

Functional Analysis · Mathematics 2020-07-20 Yassine Bedrani , Fuad Kittaneh , Mohammed Sababheh

For a square matrix, the range of its Rayleigh quotients is known as the numerical range, which is a compact and convex set by the Toeplitz-Hausdorff theorem. The largest value and the smallest boundary value (in magnitude) of this convex…

Probability · Mathematics 2025-10-06 Zhigang Bao , Giorgio Cipolloni

Let ${\mathcal B}({\mathcal H})$ be the algebra of all bounded linear operators on the Hilbert space ${\mathcal H}$. For a positive integer $k$ less than the dimension of ${\mathcal H}$ and ${\mathbf A} = (A_1, \dots, A_m)\in {\mathcal…

Functional Analysis · Mathematics 2022-05-17 Jor-Ting Chan , Chi-Kwong Li , Yiu-Tung Poon

The field of numerical algebraic geometry consists of algorithms for numerically solving systems of polynomial equations. When the system is exact, such as having rational coefficients, the solution set is well-defined. However, for a…

Numerical Analysis · Mathematics 2024-03-28 Emma R. Cobian , Jonathan D. Hauenstein , Charles W. Wampler

In a geometric $k$-clustering problem the goal is to partition a set of points in $\mathbb{R}^d$ into $k$ subsets such that a certain cost function of the clustering is minimized. We present data structures for orthogonal range-clustering…

Computational Geometry · Computer Science 2017-05-18 Mikkel Abrahamsen , Mark de Berg , Kevin Buchin , Mehran Mehr , Ali D. Mehrabi

We prove new enclosures for the spectrum of non-selfadjoint operator matrices associated with second order linear differential equations $\ddot{z}(t) + D \dot{z} (t) + A_0 z(t) = 0$ in a Hilbert space. Our main tool is the quadratic…

Spectral Theory · Mathematics 2017-03-23 Birgit Jacob , Christiane Tretter , Carsten Trunk , Hendrik Vogt

A finite set of real numbers is called convex if the differences between consecutive elements form a strictly increasing sequence. We show that, for any pair of convex sets $A, B\subset\mathbb R$, each of size $n^{1/2}$, the convex grid…

Combinatorics · Mathematics 2015-04-28 Orit E. Raz , Micha Sharir , Ilya D. Shkredov

This paper introduces and investigates the concept of the $q$-numerical range for tuples of bounded linear operators in Hilbert spaces. We establish various inequalities concerning the $q$-numerical radius associated with these operator…

Functional Analysis · Mathematics 2024-10-08 Kais Feki , Arnab Patra , Jyoti Rani , Zakaria Taki

We prove that if two closed disks X_1 and X_2 of the Riemann sphere are spectral sets for a bounded linear operator A on a Hilbert space, then the intersection X_1\cap X_2 is a complete (2+2/\sqrt{3})-spectral set for A. When the…

Spectral Theory · Mathematics 2007-12-05 Catalin Badea , Bernhard Beckermann , Michel Crouzeix

Kippenhahn's Theorem asserts that the numerical range of a matrix is the convex hull of a certain algebraic curve. Here, we show that the joint numerical range of finitely many Hermitian matrices is similarly the convex hull of a…

Algebraic Geometry · Mathematics 2021-09-28 Daniel Plaumann , Rainer Sinn , Stephan Weis

The higher rank numerical range is closely connected to the construction of quantum error correction code for a noisy quantum channel. It is known that if a normal matrix $A \in M_n$ has eigenvalues $a_1, \..., a_n$, then its higher rank…

Functional Analysis · Mathematics 2011-02-10 Hwa-Long Gau , Chi-Kwong Li , Yiu-Tung Poon , Nung-Sing Sze

The existing research on spectral algorithms, applied within a Reproducing Kernel Hilbert Space (RKHS), has primarily focused on general kernel functions, often neglecting the inherent structure of the input feature space. Our paper…

Machine Learning · Statistics 2024-03-08 Weichun Xia , Lei Shi

Krylov subspace methods are a powerful tool for efficiently solving high-dimensional linear algebra problems. In this work, we study the approximation quality that a Krylov subspace provides for estimating the numerical range of a matrix.…

Numerical Analysis · Mathematics 2024-12-02 Cecilia Chen , John Urschel