MNT Update sklearn.externals._lobpcg.lobpcg docstring according to
@@ -141,7 +141,7 @@ def lobpcg(
retLambdaHistory=False,
retResidualNormsHistory=False,
):
- """Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)
+ """Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG).
LOBPCG is a preconditioned eigensolver for large symmetric positive
definite (SPD) generalized eigenproblems.
@@ -161,7 +161,7 @@ def lobpcg(
Preconditioner to `A`; by default ``M = Identity``.
`M` should approximate the inverse of `A`.
Y : ndarray, float32 or float64, optional
- n-by-sizeY matrix of constraints (non-sparse), sizeY < n
+ An n-by-sizeY matrix of constraints (non-sparse), sizeY < n.
The iterations will be performed in the B-orthogonal complement
of the column-space of Y. Y must be full rank.
tol : scalar, optional
@@ -181,7 +181,7 @@ def lobpcg(
Returns
-------
w : ndarray
- Array of ``k`` eigenvalues
+ Array of ``k`` eigenvalues.
v : ndarray
An array of ``k`` eigenvectors. `v` has the same shape as `X`.
lambdas : list of ndarray, optional
@@ -240,7 +240,6 @@ def lobpcg(
Examples
--------
-
Solve ``A x = lambda x`` with constraints and preconditioning.
>>> import numpy as np
@@ -293,7 +292,6 @@ def lobpcg(
Note that the vectors passed in Y are the eigenvectors of the 3 smallest
eigenvalues. The results returned are orthogonal to those.
-
"""
blockVectorX = X
blockVectorY = Y
