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.har...@gmail.com> wrote: > > > > On Sun, Nov 18, 2018 at 9:24 PM Eric Wieser <wieser.eric+nu...@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. > > <snip> > > Chuck > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
