Lstsq
Lstsq
torch_lstsq(self, A)
self |
(Tensor) the matrix B |
A |
(Tensor) the m by n matrix A |
Computes the solution to the least squares and least norm problems for a full rank matrix A of size (m \times n) and a matrix B of size (m \times k).
If m ≥q n, torch_lstsq() solves the least-squares problem:
\begin{array}{ll} \min_X & \|AX-B\|_2. \end{array}
If m < n, torch_lstsq() solves the least-norm problem:
\begin{array}{llll} \min_X & \|X\|_2 & \mbox{subject to} & AX = B. \end{array}
Returned tensor X has shape (\mbox{max}(m, n) \times k). The first n rows of X contains the solution. If m ≥q n, the residual sum of squares for the solution in each column is given by the sum of squares of elements in the remaining m - n rows of that column.
The case when \eqn{m < n} is not supported on the GPU.if (torch_is_installed()) {
A = torch_tensor(rbind(
c(1,1,1),
c(2,3,4),
c(3,5,2),
c(4,2,5),
c(5,4,3)
))
B = torch_tensor(rbind(
c(-10, -3),
c(12, 14),
c(14, 12),
c(16, 16),
c(18, 16)
))
out = torch_lstsq(B, A)
out[[1]]
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