linalg_qr: Computes the QR decomposition of a matrix.

Description

Letting be or , the full QR decomposition of a matrix is defined as

Usage

linalg_qr(A, mode = "reduced")

Value

A list (Q, R).

Arguments

A: (Tensor): tensor of shape (*, m, n) where * is zero or more batch dimensions.
mode: (str, optional): one of 'reduced', 'complete', 'r'. Controls the shape of the returned tensors. Default: 'reduced'.

Details

torch:::math_to_rd(" A = QR\\mathrlap{\\qquad Q \\in \\mathbb{K}^{m \\times m}, R \\in \\mathbb{K}^{m \\times n}} ")

where is orthogonal in the real case and unitary in the complex case, and is upper triangular. When m > n (tall matrix), as R is upper triangular, its last m - n rows are zero. In this case, we can drop the last m - n columns of Q to form the reduced QR decomposition:

torch:::math_to_rd(" A = QR\\mathrlap{\\qquad Q \\in \\mathbb{K}^{m \\times n}, R \\in \\mathbb{K}^{n \\times n}} ")

The reduced QR decomposition agrees with the full QR decomposition when n >= m (wide matrix). Supports input of float, double, cfloat and cdouble dtypes. Also supports batches of matrices, and if A is a batch of matrices then the output has the same batch dimensions. The parameter mode chooses between the full and reduced QR decomposition.

If A has shape (*, m, n), denoting k = min(m, n)

mode = 'reduced' (default): Returns (Q, R) of shapes (*, m, k), (*, k, n) respectively.
mode = 'complete': Returns (Q, R) of shapes (*, m, m), (*, m, n) respectively.
mode = 'r': Computes only the reduced R. Returns (Q, R) with Q empty and R of shape (*, k, n).

Other linalg: linalg_cholesky_ex(), linalg_cholesky(), linalg_det(), linalg_eigh(), linalg_eigvalsh(), linalg_eigvals(), linalg_eig(), linalg_householder_product(), linalg_inv_ex(), linalg_inv(), linalg_lstsq(), linalg_matrix_norm(), linalg_matrix_power(), linalg_matrix_rank(), linalg_multi_dot(), linalg_norm(), linalg_pinv(), linalg_slogdet(), linalg_solve(), linalg_svdvals(), linalg_svd(), linalg_tensorinv(), linalg_tensorsolve(), linalg_vector_norm()

Examples

Run this code

if (torch_is_installed()) {
a <- torch_tensor(rbind(c(12., -51, 4), c(6, 167, -68), c(-4, 24, -41)))
qr <- linalg_qr(a)

torch_mm(qr[[1]], qr[[2]])$round()
torch_mm(qr[[1]]$t(), qr[[1]])$round()
}