In this article, we will discuss QR decomposition or QR factorization of a matrix. QR factorization of a matrix is the decomposition of a matrix say ‘A’ into ‘A=QR’ where Q is orthogonal and R is an upper-triangular matrix. We factorize the matrix using numpy.linalg.qr() function.
Syntax : numpy.linalg.qr(a, mode=’reduced’)
- a : matrix(M,N) which needs to be factored.
- mode : it is optional. It can be :
Below are some examples of how to use the above-described function :
Example 1: QR factorization of 2X2 matrix
Example 2: QR factorization of 2X4 matrix
Example 3: QR factorization of 3X3 matrix
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