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mxv001 avatar mxv001 commented on July 30, 2024

Well, I was able to figure it out! Here is the working code if anyone else has the same goal:

def square(x, y=12):
    return sum((x - 0.5) ** 2) + abs(y)


# IT WOULD BE GREAT TO USE NEVERGRAD NATIVE PARAMETERIZATION
parametrization = ng.p.Instrumentation(
  x=ng.p.Array(shape=(2,)).set_bounds(lower=-12, upper=12),
  y=ng.p.Scalar()
)

def print_candidate_and_value(optimizer, candidate, value):
    print(candidate,value)

optimizer=nevergrad.families.NonObjectOptimizer(method = 'Nelder-Mead')(parametrization=parametrization,budget=100)
optimizer.register_callback("tell", print_candidate_and_value)

for i in range(optimizer.budget):
    x = optimizer.ask()
    loss = square(*x.args, **x.kwargs)
    optimizer.tell(x, loss)

from nevergrad.

mxv001 avatar mxv001 commented on July 30, 2024

Closed

from nevergrad.

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