I was under the impression that the singular value decomposition of a square matrix would give me a (U,s,V) where U and V are inverses and s is the vector consisting of Eigenvalues. Is that the intent? Or am I hoping for too much?
It seems to me from testing the function, that at least sometimes U is the negative inverse of V. Is that intended to always be the case?
For example if M is the 1x1 matrix consisting of -4.034101137641814 then U is the 1x1 containing -1.0 and V is the 1x2 containing 1.0. Thus UV != I, consequently M^2 != U(D^2)V
Please help me understand what I should expect.
Thanks.
I was under the impression that the singular value decomposition of a square matrix would give me a
(U,s,V)
whereU
andV
are inverses ands
is the vector consisting of Eigenvalues. Is that the intent? Or am I hoping for too much?It seems to me from testing the function, that at least sometimes
U
is the negative inverse ofV
. Is that intended to always be the case?For example if
M
is the 1x1 matrix consisting of-4.034101137641814
thenU
is the 1x1 containing-1.0
andV
is the 1x2 containing1.0
. Thus UV != I, consequently M^2 != U(D^2)VPlease help me understand what I should expect. Thanks.