In almost any force sensor application, forces other than the one(s) being measured will be present. These extraneous forces will invariably cause errors in the measured values through what is called ...

Inverting a matrix is one of the most common tasks in data science and machine learning. In this article I explain why inverting a matrix is very difficult and present code that you can use as-is, or ...

Dozens of machine learning algorithms require computing the inverse of a matrix. Computing a matrix inverse is conceptually easy, but implementation is one of the most challenging tasks in numerical ...

Introduces linear algebra and matrices, with an emphasis on applications, including methods to solve systems of linear algebraic and linear ordinary differential equations. Discusses computational ...

The l₂ normalized inverse, shifted inverse, and Rayleigh quotient iterations are classic algorithms for approximating an eigenvector of a symmetric matrix. This work establishes rigorously that each ...