QR decomposition, also known as QR factorization, is a method for decomposing a matrix into the product of an orthogonal matrix and an upper-triangular matrix. It is a useful tool for solving systems of linear equations, computing the inverse of a matrix, and computing the singular value decomposition (SVD) of a matrix. In this blog […]
Numpy Tips
How to solve system of linear equations with Numpy
In this tutorial, we will learn how to solve a system of linear equations in Python using Numpy. We will see two examples, first with a system of linear equations with two unknowns and. two variables. And the with a system of linear equations with three unknowns and three equations. We will use Numpy’s linalg.solve() […]
How to do Cholesky Matrix Decomposition with Numpy
Choleski decomposition, also known as Choleski factorization, is one of the commonly used matrix decomposition methods that factorises a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose. This decomposition is widely used in scientific and engineering applications, such as linear algebra, machine learning, and optimization. In this blog […]
How to Compute Matrix inverse with Numpy
In this post, we will learn how to compute the inverse of matrix in Python using Numpy. Computing the inverse of a matrix is at the heart of linear algebra and important for many real world problem. We will use Numpy’s linalg.inv() function to to compute the inverse of a matrix in Python. Before we […]
How to Compute Manhattan Distance in Python with Numpy
In this post, we will learn how to compute Manhattan distance, one. of the commonly used distance meeasures, in Python using Numpy. Manhattan distance is also known as the “taxi cab” distance as it is a measure of distance between two points in a grid-based system like layout of the streets in Manhattan, New York […]