Thanks both! Yes, I guess it's typically 'least squares' referring to
the residual vector, and 'minimum norm' referring to the solution
vector. That's certainly how the documentation for `dgelsd` frames it.
In my case, the minimum norm solution can be sensibly interpreted (and
in particular, it gua
On Sun, Nov 18, 2018 at 9:24 PM Eric Wieser
wrote:
> > In 1.15 the call is instead to `_umath_linalg.lstsq_m` and I'm not sure
> what this actually ends up doing - does this end up being the same as
> `dgelsd`?
>
> When the arguments are real, yes. What changed is that the dispatching now
> happe
> In 1.15 the call is instead to `_umath_linalg.lstsq_m` and I'm not sure
what this actually ends up doing - does this end up being the same as
`dgelsd`?
When the arguments are real, yes. What changed is that the dispatching now
happens in C, which was done as a step towards the incomplete
https:/
Hi all,
I'm solving an underdetermined system using `numpy.linalg.lstsq` and
trying to track down its behavior for underdetermined systems. In
previous versions of numpy (e.g. 1.14) in `linalg.py` the definition
for `lstsq` calls `dgelsd` for real inputs, which I think means that
the underdetermin
