This article is based on Gilbert Strang's lecture script. The article will be described according to the flow of the lecture. I've been multilplying matrices already, but certainly time for me to ...

Abstract: The book consists of three parts. Part 1 focuses on vectors and their manipulation. Vector algebra, linear functions, linearization, inner products, norms, linear independence, the concept ...

Matrix multiplication is a fundamental operation in linear algebra and has numerous applications in various fields of science, engineering, and computation. Multiplying matrices may seem complicated ...

ABSTRACT: In this paper, a series of bicomplex representation methods of quaternion division algebra is introduced. We present a new multiplication concept of quaternion matrices, a new determinant ...

These are notes that cover a number of topics from linear algebra that I have found fundamental to master random matrices using J language. The prerequisite for fully comprehending the examples below ...

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 ...

NumPy includes some tools for working with linear algebra in the numpy.linalg module. However, unless you really don’t want to add SciPy as a dependency to your project, it’s typically better to use ...

Abstract: Sparse matrices and linear algebra are at the heart of scientific simulations. More than 70 sparse matrix storage formats have been developed over the years, targeting a wide range of ...