QR Decomposition

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In linear algebra, a QR decomposition (also called a QR factorization) of a matrix is a decomposition of the matrix into an orthogonal and a right triangular matrix. QR decomposition is often used to solve the linear least squares problem, and is the basis for a particular eigenvalue algorithm, the QR algorithm. There are several methods for actually computing the QR decomposition, such as by means of the Gram Schmidt process, Householder transformations, or Givens rotations. Each has a number of advantages and disadvantages.

Klappentext

High Quality Content by WIKIPEDIA articles! In linear algebra, a QR decomposition (also called a QR factorization) of a matrix is a decomposition of the matrix into an orthogonal and a right triangular matrix. QR decomposition is often used to solve the linear least squares problem, and is the basis for a particular eigenvalue algorithm, the QR algorithm. There are several methods for actually computing the QR decomposition, such as by means of the Gram-Schmidt process, Householder transformations, or Givens rotations. Each has a number of advantages and disadvantages.