Triton model calculation test of the BonnW-matrix rank-one approximation

1989 ◽  
Vol 40 (6) ◽  
pp. 2877-2880 ◽  
Author(s):  
B. F. Gibson ◽  
B. C. Pearce ◽  
G. L. Payne
1991 ◽  
Vol 43 (6) ◽  
pp. 2897-2897
Author(s):  
B. F. Gibson ◽  
B. C. Pearce ◽  
G. L. Payne

2009 ◽  
Vol 128 (1-2) ◽  
pp. 253-283 ◽  
Author(s):  
Wenbao Ai ◽  
Yongwei Huang ◽  
Shuzhong Zhang

2017 ◽  
Vol 29 (5) ◽  
pp. 1317-1351 ◽  
Author(s):  
Shashanka Ubaru ◽  
Yousef Saad ◽  
Abd-Krim Seghouane

Many machine learning and data-related applications require the knowledge of approximate ranks of large data matrices at hand. This letter presents two computationally inexpensive techniques to estimate the approximate ranks of such matrices. These techniques exploit approximate spectral densities, popular in physics, which are probability density distributions that measure the likelihood of finding eigenvalues of the matrix at a given point on the real line. Integrating the spectral density over an interval gives the eigenvalue count of the matrix in that interval. Therefore, the rank can be approximated by integrating the spectral density over a carefully selected interval. Two different approaches are discussed to estimate the approximate rank, one based on Chebyshev polynomials and the other based on the Lanczos algorithm. In order to obtain the appropriate interval, it is necessary to locate a gap between the eigenvalues that correspond to noise and the relevant eigenvalues that contribute to the matrix rank. A method for locating this gap and selecting the interval of integration is proposed based on the plot of the spectral density. Numerical experiments illustrate the performance of these techniques on matrices from typical applications.


2011 ◽  
Vol 11 (3) ◽  
pp. 291-304 ◽  
Author(s):  
Lars Grasedyck ◽  
Wolfgang Hackbusch

Abstract We review two similar concepts of hierarchical rank of tensors (which extend the matrix rank to higher order tensors): the TT-rank and the H-rank (hierarchical or H-Tucker rank). Based on this notion of rank, one can define a data-sparse representation of tensors involving O(dnk + dk^3) data for order d tensors with mode sizes n and rank k. Simple examples underline the differences and similarities between the different formats and ranks. Finally, we derive rank bounds for tensors in one of the formats based on the ranks in the other format.


1987 ◽  
Vol 84 ◽  
pp. 855-861 ◽  
Author(s):  
M. Flórez ◽  
M. Bermejo ◽  
V. Luaña ◽  
E. Francisco ◽  
J.M. Recio ◽  
...  

1970 ◽  
Vol 11 (8) ◽  
pp. 2415-2424 ◽  
Author(s):  
M. Anthea Grubb ◽  
D. B. Pearson

1972 ◽  
Vol 46 ◽  
pp. 97-109
Author(s):  
Susan Williamson

Let k denote the quotient field of a complete discrete rank one valuation ring R of unequal characteristic and let p denote the characteristic of R̅; assume that R contains a primitive pth root of unity, so that the absolute ramification index e of R is a multiple of p — 1, and each Gallois extension K ⊃ k of degree p may be obtained by the adjunction of a pth root.


2020 ◽  
Vol 11 (2) ◽  
pp. 1-33
Author(s):  
Haibing Lu ◽  
Xi Chen ◽  
Junmin Shi ◽  
Jaideep Vaidya ◽  
Vijayalakshmi Atluri ◽  
...  

Author(s):  
Constanze Liaw ◽  
Sergei Treil ◽  
Alexander Volberg

Abstract The classical Aronszajn–Donoghue theorem states that for a rank-one perturbation of a self-adjoint operator (by a cyclic vector) the singular parts of the spectral measures of the original and perturbed operators are mutually singular. As simple direct sum type examples show, this result does not hold for finite rank perturbations. However, the set of exceptional perturbations is pretty small. Namely, for a family of rank $d$ perturbations $A_{\boldsymbol{\alpha }}:= A + {\textbf{B}} {\boldsymbol{\alpha }} {\textbf{B}}^*$, ${\textbf{B}}:{\mathbb C}^d\to{{\mathcal{H}}}$, with ${\operatorname{Ran}}{\textbf{B}}$ being cyclic for $A$, parametrized by $d\times d$ Hermitian matrices ${\boldsymbol{\alpha }}$, the singular parts of the spectral measures of $A$ and $A_{\boldsymbol{\alpha }}$ are mutually singular for all ${\boldsymbol{\alpha }}$ except for a small exceptional set $E$. It was shown earlier by the 1st two authors, see [4], that $E$ is a subset of measure zero of the space $\textbf{H}(d)$ of $d\times d$ Hermitian matrices. In this paper, we show that the set $E$ has small Hausdorff dimension, $\dim E \le \dim \textbf{H}(d)-1 = d^2-1$.


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