Results 61 to 70 of about 2,074 (253)

Khatri-Rao Products for Operator Matrices Acting on the Direct Sum of Hilbert Spaces

Journal of Mathematics, 2016
We introduce the notion of Khatri-Rao product for operator matrices acting on the direct sum of Hilbert spaces. This notion generalizes the tensor product and Hadamard product of operators and the Khatri-Rao product of matrices.
Arnon Ploymukda, Pattrawut Chansangiam
doaj   +1 more source

Bulk behavior of Schur–Hadamard products of symmetric random matrices

, 2014
We develop a general method for establishing the existence of the Limiting Spectral Distributions (LSD) of Schur–Hadamard products of independent symmetric patterned random matrices. We apply this method to show that the LSD of Schur–Hadamard products of
Bose, Arup, Mukherjee, Soumendu Sundar
core   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

Hadamard Products of Projective Varieties with Errors and Erasures

AppliedMath
In Algebraic Statistics, M.A. Cueto, J. Morton and B. Sturmfels introduced a statistical model, the Restricted Boltzmann Machine, which introduced the Hadamard product of two or more vectors of an affine or projective space, i.e., the componentwise ...
Edoardo Ballico
doaj   +1 more source

Some determinantal inequalities for Hadamard and Fan products of matrices

Journal of Inequalities and Applications, 2016
In this note, we generalize some determinantal inequalities which are due to Lynn (Proc. Camb. Philos. 60:425-431, 1964), Chen (Linear Algebra Appl. 368:99-106, 2003) and Ando (Linear Multilinear Algebra 8:291-316, 1980).
Xiaohui Fu, Yang Liu
doaj   +1 more source

Hadamard Products and Tilings

, 2009
Louis W. Shapiro gave a combinatorial proof of a bilinear generating function for Chebyshev polynomials equivalent to the formula 1/(1-ax-x^2) * 1/(1-bx-x^2) = (1-x^2)/(1-abx-(2+a^2+b^2)x^2 -abx^3+x^4), where * denotes the Hadamard product. In a similar way, by considering tilings of a 2 by n rectangle with 1 by 1 and 1 by 2 bricks in the top row, and ...
openaire   +3 more sources

Partitioned and Hadamard product matrix inequalities [PDF]

Journal of Research of the National Bureau of Standards, 1978
This note is partly expositor). Inequalities relating inversion with, respectively, extraction of principal submatriees and the Hadamard product in the two possible orders are developed in a simple and unified way for positive definite matrices. These inequalities are known, hut we also characterize the cases of equality and strict inequality.
openaire   +3 more sources

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Advanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci, KyuJung Jun, Bowen Deng, Gerbrand Ceder   +3 more
wiley   +1 more source

Hadamard products and golden - thompson type inequalities

, 1996
By using Hadamard products we give some reasonable upper and lower bounds of Golden-Thompson type for ∥eH1 +…+ Hm∥, where Hi(i = 1, 2, …, m) are arbitrary Hermitian matrices and ∥·∥ is an arbitrary unitarily invariant ...
Ando, T.
core   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source