Welcome to the nlp-2.1-matrix-decomposition repository! This project provides a collection of algorithms for matrix decomposition, a fundamental concept in linear algebra. Whether you're working on ...

Matrix factorization techniques have become pivotal in data mining, enabling the extraction of latent structures from large-scale data matrices. These methods decompose complex datasets into ...

Abstract: LU-Net is a simple and fast architecture for invertible neural networks (INN) that is based on the factorization of quadratic weight matrices A=LU, where L is a lower triangular matrix with ...

3. Iterative Methods for solving the EigenValue Problem: Iterative Methods known for solving the eigenvalue problem are: Rayleigh Quotient Iteration: finds the eigenvector and eigenvalue pair closest ...

Abstract: This article analyzes the composition and characteristics of echo signals in a pseudorandom-coded ground-penetrating radar (GPR). Based on these characteristics, an innovative low-rank ...

An important problem in multivariate statistics is the estimation of covariance matrices. We consider a class of nonparametric covariance models in which the entries in the covariance matrix depend on ...

Asynchronous Many-Task Systems and Applications: Second International Workshop, WAMTA 2024, Knoxville, TN, USA, February 14–16, 2024 The ubiquitous in-node heterogeneity of HPC and cloud computing ...