spyrit.core.recon.Tikhonov.divide

Tikhonov.divide(y: tensor, gamma: tensor) → tensor

Computes the division \(y \cdot (\sigma_lpha imes noisescale + (A \Sigma A^T))^{-1}\).

Measurements y are divided by the sum of the measurement covariance.

If self.approx is True, the inverse is approximated as a diagonal matrix, speeding up the computation. Otherwise, the inverse is computed with the whole matrix.

Args:

y (torch.tensor): Input measurement tensor. Shape \((*, M)\).

gamma (torch.tensor): Noise covariance tensor. Shape \((*, M, M)\).

Returns:

torch.tensor: The divided tensor. Shape \((*, M)\).