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The history of research on eigenvalue problems is rich with many outstanding contributions. Nonetheless, the rapidly increasing size of data sets requires new algorithms for old problems in the context of extremely large matrix dimensions.…

Distributed, Parallel, and Cluster Computing · Computer Science 2013-12-17 Hesam T. Dashti , Alireza F. Siahpirani , Liya Wang , Mary Kloc , Amir H. Assadi

Understanding the singular value spectrum of a matrix $A \in \mathbb{R}^{n \times n}$ is a fundamental task in countless applications. In matrix multiplication time, it is possible to perform a full SVD and directly compute the singular…

Data Structures and Algorithms · Computer Science 2019-01-04 Cameron Musco , Praneeth Netrapalli , Aaron Sidford , Shashanka Ubaru , David P. Woodruff

We calculate the eigenvalues of some two-dimensional non-Hermitian Hamiltonians by means of a pseudospectral method and straightforward diagonalization of the Hamiltonian matrix in a suitable basis set. Both sets of results agree remarkably…

Quantum Physics · Physics 2014-03-19 Paolo Amore , Francisco M. Fernández , Javier Garcia

This paper presents a fast and powerful method for the computation of eigenvalue bounds for Hessian matrices $\nabla^2 \varphi(x) $ of nonlinear functions $\varphi: U \subseteq R^n\rightarrow R$ on hyperrectangles $B \subset U$. The method…

Optimization and Control · Mathematics 2015-07-23 Moritz Schulze Darup , Martin Mönnigmann

This paper establishes a new comparison principle for the minimum eigenvalue of a sum of independent random positive-semidefinite matrices. The principle states that the minimum eigenvalue of the matrix sum is controlled by the minimum…

Probability · Mathematics 2025-01-29 Joel A. Tropp

We study ill-conditioned positive definite matrices that are disturbed by the sum of $m$ rank-one matrices of a specific form. We provide estimates for the eigenvalues and eigenvectors. When the condition number of the initial matrix tends…

Numerical Analysis · Mathematics 2024-03-13 Armand Gissler , Anne Auger , Nikolaus Hansen

In this paper we propose some very promissing results in interval arithmetics which permit to build well-defined arithmetics including distributivity of multiplication and division according addition and substraction. Thus, it allows to…

Numerical Analysis · Computer Science 2011-07-20 Nicolas Goze , Michel Goze , Abdel Kenoufi , Elisabeth Remm

We characterize the relationship between the singular values of a complex Hermitian (resp., real symmetric, complex symmetric) matrix and the singular values of its off-diagonal block. We also characterize the eigenvalues of an Hermitian…

Algebraic Geometry · Mathematics 2007-05-23 Sergey Fomin , William Fulton , Chi-Kwong Li , Yiu-Tung Poon

The aim of this article is to provide a simple and unified way to obtain the sharp upper bounds of nodal sets of eigenfunctions for different types of eigenvalue problems on real analytic domains. The examples include biharmonic Steklov…

Analysis of PDEs · Mathematics 2020-10-08 Fanghua Lin , Jiuyi Zhu

We present here the necessary and sufficient conditions for the invertibility of tridiagonal matrices, commonly named Jacobi matrices, and explicitly compute their inverse. The techniques we use are related with the solution of…

Rings and Algebras · Mathematics 2018-07-23 A. M. Encinas , M. J. Jiménez

We study analogues of classical inequalities for the eigenvalues of sums of pseudo-Hermitian matrices.

Rings and Algebras · Mathematics 2008-05-09 Philip Foth

Eigenvalue and eigenpair backward errors are computed for matrix pencils arising in optimal control. In particular, formulas for backward errors are developed that are obtained under block-structure-preserving and…

Numerical Analysis · Mathematics 2017-12-25 Christian Mehl , Volker Mehrmann , Punit Sharma

This work introduces the minimax Laplace transform method, a modification of the cumulant-based matrix Laplace transform method developed in "User-friendly tail bounds for sums of random matrices" (arXiv:1004.4389v6) that yields both upper…

Probability · Mathematics 2011-07-22 Alex Gittens , Joel A. Tropp

This paper presents a Jacobi-type iteration for computing a given specified eigenpair of a symmetric matrix. For a certain class of diagonally dominant matrices, the procedure is shown to converge at a linear rate depending on how the…

Numerical Analysis · Mathematics 2026-05-26 Luca Gemignani

We establish precise right-tail small deviation estimates for the largest eigenvalue of real symmetric and complex Hermitian matrices whose entries are independent random variables with uniformly bounded moments. The proof relies on a Green…

Probability · Mathematics 2022-04-04 László Erdős , Yuanyuan Xu

This letter derives some new exponential bounds for discrete time, real valued, conditionally symmetric martingales with bounded jumps. The new bounds are extended to conditionally symmetric sub/ supermartingales, and they are compared to…

Probability · Mathematics 2013-05-02 Igal Sason

We extend the results given by Colbois, Dryden and El Soufi on the relationships between the eigenvalues of the Laplacian and an extrinsic invariant called intersection index, in two directions. First, we replace this intersection index by…

Spectral Theory · Mathematics 2013-04-30 Asma Hassannezhad

We discuss an approach to compute the first and second moments of the number of eigenvalues $I_N$ that lie in an arbitrary interval of the real line for $N \times N$ Gaussian random matrices. The method combines the standard…

Statistical Mechanics · Physics 2017-11-22 Fernando L. Metz

A family of tridiagonal pairs which appear in the context of quantum integrable systems is studied in details. The corresponding eigenvalue sequences, eigenspaces and the block tridiagonal structure of their matrix realizations with respect…

Mathematical Physics · Physics 2015-06-26 Pascal Baseilhac

We improve proofs in "The Floyd-Warshall Algorithm, the AP and the TSP (III). We also simplify the method for obtaining a good upper bound for an optimal solution.

Combinatorics · Mathematics 2007-05-23 Howard Kleiman
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