Following you constrained optimization example in docs:
def square(x):
return sum((x - 0.5) ** 2)
optimizer = ng.optimizers.NGOpt(parametrization=2, budget=100)
# define a constraint on first variable of x:
optimizer.parametrization.register_cheap_constraint(lambda x: x[0] >= 1)
recommendation = optimizer.minimize(square, verbosity=2)
print(recommendation.value)
# >>> [1.00037625, 0.50683314]
Is this the most efficient way of performing the optimization or would it be better to incorporate the same constraint into the objective function (with an if clause that returns inf or very large value)? What are the fundamental differences of these two methods in terms of computation and the way optimization works?
Following you constrained optimization example in docs:
Is this the most efficient way of performing the optimization or would it be better to incorporate the same constraint into the objective function (with an
ifclause that returnsinfor very large value)? What are the fundamental differences of these two methods in terms of computation and the way optimization works?