- paddle.linalg. qr ( x, mode='reduced', name=None ) [source]
Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).
x (Tensor) – The input tensor. Its shape should be […, M, N], where … is zero or more batch dimensions. M and N can be arbitrary positive number. The data type of x should be float32 or float64.
mode (str, optional) – A flag to control the behavior of qr, the default is “reduced”. Suppose x’s shape is […, M, N] and denoting K = min(M, N): If mode = “reduced”, qr op will return reduced Q and R matrices, which means Q’s shape is […, M, K] and R’s shape is […, K, N]. If mode = “complete”, qr op will return complete Q and R matrices, which means Q’s shape is […, M, M] and R’s shape is […, M, N]. If mode = “r”, qr op will only return reduced R matrix, which means R’s shape is […, K, N].
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.
If mode = “reduced” or mode = “complete”, qr will return a two tensor-tuple, which represents Q and R. If mode = “r”, qr will return a tensor which represents R.
import paddle x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64') q, r = paddle.linalg.qr(x) print (q) print (r) # Q = [[-0.16903085, 0.89708523], # [-0.50709255, 0.27602622], # [-0.84515425, -0.34503278]]) # R = [[-5.91607978, -7.43735744], # [ 0. , 0.82807867]]) # one can verify : X = Q * R ;