Easy as possible to use

[juan43ramirez]

Enhancement

Make Cooper as easy as possible to integrate with a vanilla Pytorch pipeline.

In particular, allow for the user to populate a CMPState and provide it to Cooper without having to create a custom ConstrainedMinimizationProblem.

Note that we should not remove the CMP altogether. In particular, extrapolation and alternating updates would still require internal calls to cmp.closure and cmp.defect_fn.

This would require a major overhaul of the documentation, tutorials, and examples, showcasing that both the previous CMP approach and this new approach are admissible. Love for the documentation has also been requested in #53 and #29.

Motivation

Users are currently required to implement a custom ConstrainedMinimizationProblem with a closure method.
This overhead can be detrimental to Cooper attracting ML researchers and practitioners who would otherwise compute the loss and constraint violations themselves.

For minimal integrations with Cooper which do not need "fancy features" like Augmented Lagrangian or Extrapolation, simply asking for the loss and constraint defects makes the user's life easier.

Alternatives

It may be possible to remove the CMP completely. Nonetheless, we would still require a closure for an internal call during extrapolation steps and a defect_fc for internal use by the AlternatingConstrainedOptimizer.

[gallego-posada]

Closed by #56