Add ConstrainedQuadraticModel.add_linear_constraints() method

[arcondello]

Speeds up adding linear constraints to the CQM. We get about a 10x speedup.

import time

import dimod
import numpy as np

num_variables = 1_000
num_constraints = 5_000
pnz = .25  # probability that a bias is non-zero

rng = np.random.default_rng(42)

A = rng.uniform(size=(num_constraints, num_variables))
A[A > pnz] = 0
b = rng.uniform(size=num_constraints)

####################

t = time.perf_counter()

cqm = dimod.ConstrainedQuadraticModel()
cqm.add_variables("BINARY", num_variables)

ci = 0
for rhs, lhs in zip(A, b):
    cqm.add_constraint(((v, bias) for v, bias in enumerate(rhs) if bias), '==', lhs, label=ci)
    ci += 1

print(f"old: {time.perf_counter() - t}")

old = cqm

###################

t = time.perf_counter()

cqm = dimod.ConstrainedQuadraticModel()
cqm.add_variables("BINARY", num_variables)
cqm.add_linear_constraints(A, '==', b, constraint_labels=range(num_constraints))

print(f"new: {time.perf_counter() - t}")

Gives

old: 0.6515062270009366
new: 0.07133342499946593

[codecov-commenter]

Codecov Report

Merging #1307 (cc6e1e7) into main (8952fe8) will decrease coverage by 0.01%.
The diff coverage is 84.61%.

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@@            Coverage Diff             @@
##             main    #1307      +/-   ##
==========================================
- Coverage   91.70%   91.69%   -0.01%     
==========================================
  Files          94       94              
  Lines       10135    10147      +12     
==========================================
+ Hits         9294     9304      +10     
- Misses        841      843       +2     

Impacted Files	Coverage Δ
dimod/constrained/constrained.py	92.88% <84.61%> (-0.20%)	⬇️

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[JoelPasvolsky]

Just looked at the docstring so far, I can get back to the code later if needed

[JoelPasvolsky]

Suggested change

	Add linear constraints defined by
	Add linear constraints defined by the following upper-boundary and equality conditions

to clarify ub and eq

[JoelPasvolsky]

Suggested change

	where `x` is a vector of decision variables,
	where :math:`x` is a vector of decision variables,

[arcondello]

It's the same formatting, you only need to use :math: when you want to use latex etc.

[JoelPasvolsky]

Do you mean here something like, "Labels are generated for any unlabeled constraints"?

[JoelPasvolsky]

Suggested change

	We can also use :class:`str`-labelled variables
	You can also label the variables:

Is it helpful to point out that labels are strings? Would they be something else?

[arcondello]

I am distinguishing them from the int-labelled ones from before, but I can clarify the intent

[mhramani]

LGTM, maybe useful to add something like:
`cqm.add_linear_objective(c, variable_labels=...)'