Abstract: This book contains a detailed discussion of the matrix operation, its properties, and its applications in finding the solution of linear equations and determinants. Linear algebra is a ...

The study of condition numbers and perturbation analysis in least squares problems has become a cornerstone in numerical linear algebra, underpinning the reliability and accuracy of solutions to ...

Recently, Wei in [18] proved that perturbed stiff weighted pseudoinverses and stiff weighted least squares problems are stable, if and only if the original and perturbed coefficient matrices A and Ā ...

Studying linear algebra effectively involves both theoretical understanding and practical problem-solving. Here’s a recommended approach and some well-regarded textbooks to guide you: Welcome to MIT ...

ABSTRACT: Through the real representations of quaternion matrices and matrix rank method, we give the expression of the real ma-trices in least-squares g-inverse and minimum norm g-inverse. From these ...

This is a preview. Log in through your library . Abstract Based on the generalized minimal residual (GMRES) principle, Hu and Reichel proposed a minimal residual algorithm for the Sylvester equation.

A General Discussion of the Algorithm : The Segmented Least Squares (SLS) algorithm is a powerful technique used to model data points by dividing them into segments and fitting a linear regression ...