More performant handling of linear constraints in CQM

[arcondello]

For large CQMs with lots of linear constraints, the user can run into memory/performance bottlenecks. One place we could potentially relieve the bottleneck is to not encode the linear constraints as quadratic models. I see a few possible approaches:

1. Create a new LinearModel object or similar.

• Pro: relatively easy to implement
• Pro: utilizes the current structure, in that it just adds another model type. We already support BQM/QM
• Con: each linear constraint is its own object, which has a memory cost. Even worse, each linear model keeps its own Variables object which is redundant and potentially expensive from a memory perspective

2. Store linear constraints in a matrix or sparse matrix, something like LinearConstraints class

• Pro: compact
• Pro: shared variable labels
• Con: big change to the structure
• Con: we probably can't use scipy sparse matrices of numpy arrays, because we want them to be resizeable (mitigated with some pretty simple cython)

In either case I think we can maintain serialization compatibility by just continuing to serialize them as quadratic models