Singular Value Decomposition (SVD)
Introduction Singular Value Decomposition (SVD) is a matrix factorization technique that is widely used in linear algebra, statistics, signal processing,
Linear Algebra, YEM
Linear Algebra
Collection of resources for key topics in Linear Algebra. Designed to understand the foundational concepts of Linear Algebra- vector and matrix operations to eigenvalues and matrix decompositions.
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Matrices of derivatives
Gradient Consider a scalar valued function $f(\text{X})$ defined on $n$ variables; that is, $f: \mathbb{R}^n \to \mathbb{R}$, where $\text{X}=(x_1,x_2,\cdots, x_n)^T$
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Eigen Values and Eigen Vectors
Eigen value An eigen value of a square matrix $A = [a_{ij}]$ is a scalar $\lambda$ such that there exists
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Vectors: Definition, Operations, and Properties
Definition of a Vector A vector is an ordered collection of numbers, which are called the components or entries of
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Special Types of Matrices
1. Diagonal Matrix A diagonal matrix is a square matrix where all elements outside the main diagonal are zero. For
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Introduction to Matrices
Definition of a Matrix A matrix is a rectangular array of numbers or symbols arranged in rows and columns. Matrices