1. QR decompositionIn linear algebra, a QR decomposition (also called a QR factorization) of a matrix is a decomposition of a matrix A into a product A=QR of an orthogonal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares problem, and is the basis for a particular eigenvalue algorithm, the QR algorithm. If A has linearly independent columns (say n columns), then the first n columns of Q form an orthonormal basis for the column space of A.
Read “QR decomposition” on English Wikipedia
Read “QR分解” on Japanese Wikipedia
Read “QR decomposition” on DBpedia
Read “QR decomposition” on English Wikipedia
Read “QR分解” on Japanese Wikipedia
Read “QR decomposition” on DBpedia
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