Speed up sol[UnaryExpr] via C-level math and refactor UnaryExpr

[Zeroto521]

This branch is 1.45x faster than master.

• master branch: 2.9803s
• this branch: 4.3398s

from timeit import timeit

from pyscipopt import Model, sqrt

m = Model()

n = 1000
x = m.addMatrixVar(n, lb=0, ub=1, vtype="C", name="x")
e = sqrt(x)
obj = m.addVar(lb=0, ub=None, vtype="C", name="obj")
m.addCons(obj == e.sum())

m.setObjective(obj, "maximize")
m.hideOutput()
m.optimize()
sol = m.getBestSol()

number = 10_000
cost = timeit(lambda: sol[e], number=number)
print(f"Cost of squaring an expression over {number:,} runs: {cost:.4f} seconds")

[Joao-Dionisio]

By the way, @Zeroto521 , out of curiosity. Are you also using PySCIPOpt for your work, or are you just speeding things up for fun?

[Zeroto521]

80% of the OR problems I encounter use commercial solvers or our closure algorithms for large-scale, speed-critical cases, while the rest use open-source ones for small tasks and research.

[Zeroto521]

ExprLike.__abs__ can replace UnaryExpr(Operator.fabs, self)