Add constraints for optimization problems

[JingyuQian]

🚀 Feature

Don't know if this has been raised before or should be the purpose of this lib, but it would be nice to allow adding constraints for an optimization problem.

Motivation

Some optimization problems take on the form:

$$min\text{ } F(x,y,z),\text{ } s.t. \text{ }x\leq\alpha,...$$

while $F(x,y,z)$ is easy to implement as:

$$F(x,y,z)=\sum_i[w_ig_i(x,y,z)]^2$$

the constraints don't have an explicit way to implement. One alternative is to write it as a normal loss and give a relatively large $w_i$, but I found it in practice this doesn't work very well.

Pitch

Something like the following

objective = th.Objective()
# Add losses
objective.add_loss(loss_function)
...
# Add constraints
objective.add_constraint(constraint_function)

Additional context

The project casadi has something as

from casadi import Opti
optim = Opti()
optim.subject_to(optim.bounded(lower_bound, x, upper_bound))

[luisenp]

Hi @JingyuQian. Thanks for your interest in Theseus and the feature request!

We are in fact starting to work on this feature, and there is an experimental PR up #457 (only for equality constraints). It's still pretty early in development, but we are hoping to have something running within the next two months.

[JingyuQian]

Thanks @luisenp ! For my use case, inequality constraints are required, but this is good news to hear. I'll close this and keep watching the PR.