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Currently, solving a simple least squares problem, min_x || Ax - b ||, in SCICO requires calling conjugate gradient on ATA and ATb. For a user without much inverse problem experience, this may not be intuitive. Let's add a solver like numpy.linalg.lstsq that takes A and b as input.
Easy approach: just make a wrapper around the existing CG.
The text was updated successfully, but these errors were encountered:
* Add docs and implementation of parameter that had no effect
* Add least squares solver function
* Docs fix
* Move support for optional x0 into cg function
* Resolve mypy error
* Typo fix
Currently, solving a simple least squares problem, min_x || Ax - b ||, in SCICO requires calling conjugate gradient on ATA and ATb. For a user without much inverse problem experience, this may not be intuitive. Let's add a solver like
numpy.linalg.lstsq
that takes A and b as input.Easy approach: just make a wrapper around the existing CG.
The text was updated successfully, but these errors were encountered: