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 guarantees that the solution is 0 for missing variables), so it's great to know that I can rely on this being returned Cheers, Romesh On Mon, Nov 19, 2018 at 12:30 PM Charles R Harris <charlesr.harris@gmail.com> wrote:
On Sun, Nov 18, 2018 at 9:24 PM Eric Wieser <wieser.eric+numpy@gmail.com> 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 happens in C, which was done as a step towards the incomplete https://github.com/numpy/numpy/issues/8720.
I'm not an expert - but aren't "minimum norm" and "least squares" two ways to state the same thing?
If there aren't enough data points to uniquely determine the minimizing solution, the solution vector of shortest length is returned. In practice it is pretty useless because it depends on the column scaling and there is generally no natural metric in the solution space.
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