torch.lobpcg
torch.lobpcg(A, k=None, B=None, X=None, n=None, iK=None, niter=None, tol=None, largest=None, method=None, tracker=None, ortho_iparams=None, ortho_fparams=None, ortho_bparams=None)
Find the k largest (or smallest) eigenvalues and the corresponding
   eigenvectors of a symmetric positive definite generalized
   eigenvalue problem using matrix-free LOBPCG methods.
This function is a front-end to the following LOBPCG algorithms
   selectable via method argument:
  *method="basic"* - the LOBPCG method introduced by Andrew
  Knyazev, see [Knyazev2001]. A less robust method, may fail when
  Cholesky is applied to singular input.

  *method="ortho"* - the LOBPCG method with orthogonal basis
  selection [StathopoulosEtal2002]. A robust method.

Supported inputs are dense, sparse, and batches of dense matrices.
Note:
 In general, the basic method spends least time per iteration.
 However, the robust methods converge much faster and are more
