Multiple Primal Optimizers

[juan43ramirez]

Closes #39

Changes

Parameter primal_optimizer of ConstrainedOptimizer renamed to primal_optimizers. It accepts either a (i) torch.optim.Optimizer or a (ii) list of Optimizers. Behavior in case (i) is unchanged.

Lines previously performing constrained_optimizer.primal_optimizer.method() have been replaced by for primal_optimizer in constrained_optimizer.primal_optimizers; primal_optimizer.method().
For instance,

cooper/cooper/constrained_optimizer.py

Lines 329 to 330 in 4421115

	for primal_optimizer in self.primal_optimizers:
	primal_optimizer.step()

Saving and loading of checkpoints was modified to allow for lists of primal optimizers.

⚠ Warning ⚠
This breaks backward compatibility when instantiating a ConstrainedOptimizer with keyword argument primal_optimizer.

Testing

Toy2D testing of constrained and unconstrained execution included in test_optimizer.py.
Checkpointing test included in test_checkpoint.py

This functionality is not tested together with other optimization methods or formulations (Extragradient, Augmented Lagrangian, Proxy constraints).

Docs

Note added in constrained_optimizer.rst indicating that functionality exists.
Small section added in optim.rst describing how to setup multiple primal optimizers and how Cooper handles them.
Docstrings and overall documentation updated to be general enough for multiple optimizers.

[gallego-posada]

The implementation of multiple primal optimizers looks good!

Left several repeated change requests for not wrapping a single optimizer object into a list (since the ConstrainedOptimizer class would already do that internally anyway).
IMO this would keep the usage less cluttered since users will most often use single primal optimizers anyway.

[gallego-posada]

A natural follow-up feature for this would be multiple (partially instantiated) dual optimizers.
It might be tricky to allow for too much granularity (say a different optimizer for each constraint). But allowing for different optimizers for equality and inequality constraints could be a good first step.

However, this does not seem to be a pressing feature to implement at the moment.