The 2.1 API docs for the Singular Value Decomposition say:
The size p depends on the chosen algorithm:
for full SVD, p is n,
for compact SVD, p is the rank r of the matrix (i. e. the number of
positive singular values),
for truncated SVD p is min(r, t) where t is user-specified.
but I don't
Bruce A Johnson a écrit :
The 2.1 API docs for the Singular Value Decomposition say:
The size p depends on the chosen algorithm:
for full SVD, p is n,
for compact SVD, p is the rank r of the matrix (i. e. the number of
positive singular values),
for truncated SVD p is min(r, t) where t
Bruce,
The 2.1 API docs for the Singular Value Decomposition say:
The size p depends on the chosen algorithm:
for full SVD, p is n,
for compact SVD, p is the rank r of the matrix (i. e. the number of
positive singular values),
for truncated SVD p is min(r, t) where t is user-specified.
but
Dimitri Pourbaix a écrit :
Bruce,
The 2.1 API docs for the Singular Value Decomposition say:
The size p depends on the chosen algorithm:
for full SVD, p is n,
for compact SVD, p is the rank r of the matrix (i. e. the number of
positive singular values),
for truncated SVD p is min(r, t)
