QR Decomposition is a mathematical technique used in linear algebra to factor a matrix into two components: an orthogonal matrix Q and an upper triangular matrix R. This decomposition is particularly useful for solving linear systems, least squares problems, and eigenvalue computations. The orthogonal matrix Q contains orthonormal vectors, which means its columns are perpendicular and have a unit length. The process of QR Decomposition can be achieved through various methods, including the Gram-Schmidt process and Householder reflections. By breaking down a matrix in this way, it simplifies many calculations and provides insights into the properties of the original matrix, making it a valuable tool in numerical analysis and applied mathematics.