Constraints on FixedParameters are not allowed

[Fa20]

ax_client.create_experiment( name="multi_objective_optimization", parameters=[{"name": "x11", "type": "fixed", "value": 500, "value_type": "int"}, {"name": "x1", "type": "range", "bounds": [-1, 3], "value_type": "int"}, {"name": "x2", "type": "range", "bounds": [-2, 2], "value_type": "int"}, {"name": "x3", "type": "range", "bounds": [-2.0, 2.0], "value_type": "float"}, {"name": "x4", "type": "range", "bounds": [-20, 20], "value_type": "int"} ], objectives={ "objective_1": ObjectiveProperties(minimize=True),
"objective_2": ObjectiveProperties(minimize=False), }, parameter_constraints=[ "x4 - 1.5x3 >= 0", # Constraint involving x4 and x3 "x2 - 5x1 <= 0", # Constraint involving x2 and x1 "x11 - x3 >= 0", ]. How can I fix this problem in case that I want to keep this constarined "x11 - x3 >= 0" and x11 has a fixed value

another question: does AX support complex constarined like : dist = 100 / x4 length = ((x2 / np.tan(np.radians(x3))) / (x11 math.pi) 100)

constarined= (length / dist) >= 3

[bernardbeckerman]

Hi @Fa20, Can you manually input the fixed bavlue of x11 into your constraint? So your example becomes:

ax_client.create_experiment(
    name="multi_objective_optimization",
    parameters=[
        {"name": "x11", "type": "fixed", "value": 500, "value_type": "int"},
        {"name": "x1", "type": "range", "bounds": [-1, 3], "value_type": "int"},
        {"name": "x2", "type": "range", "bounds": [-2, 2], "value_type": "int"},
        {"name": "x3", "type": "range", "bounds": [-2.0, 2.0], "value_type": "float"},
        {"name": "x4", "type": "range", "bounds": [-20, 20], "value_type": "int"}
    ],
    objectives={
        "objective_1": ObjectiveProperties(minimize=True),
        "objective_2": ObjectiveProperties(minimize=False),
    },
    parameter_constraints=[
        "x4 - 1.5*x3 >= 0", # Constraint involving x4 and x3
        "x2 - 5*x1 <= 0", # Constraint involving x2 and x1
        "x3 <= 500", # This line is updated to use the fixed value of x11
    ],
)

another question: does AX support complex constarined like :

Ax only supports linear constraints, since those are easily passed down to the modeling layer and incorporated into the optimization. One imperfect-but-potentially-useful way to incorporate these constraints is as OutcomeConstraints, i.e., supply the scalar you'd like to constrain as an outcome of your experiment, and then set constraints on that scalar's value via outcome constraints (pointer). Hope that helps!

[Fa20]

@bernardbeckerman

Hi @Fa20, Can you manually input the fixed bavlue of x11 into your constraint? So your example becomes:

ax_client.create_experiment(
    name="multi_objective_optimization",
    parameters=[
        {"name": "x11", "type": "fixed", "value": 500, "value_type": "int"},
        {"name": "x1", "type": "range", "bounds": [-1, 3], "value_type": "int"},
        {"name": "x2", "type": "range", "bounds": [-2, 2], "value_type": "int"},
        {"name": "x3", "type": "range", "bounds": [-2.0, 2.0], "value_type": "float"},
        {"name": "x4", "type": "range", "bounds": [-20, 20], "value_type": "int"}
    ],
    objectives={
        "objective_1": ObjectiveProperties(minimize=True),
        "objective_2": ObjectiveProperties(minimize=False),
    },
    parameter_constraints=[
        "x4 - 1.5*x3 >= 0", # Constraint involving x4 and x3
        "x2 - 5*x1 <= 0", # Constraint involving x2 and x1
        "x3 <= 500", # This line is updated to use the fixed value of x11
    ],
)

another question: does AX support complex constarined like :

Ax only supports linear constraints, since those are easily passed down to the modeling layer and incorporated into the optimization. One imperfect-but-potentially-useful way to incorporate these constraints is as OutcomeConstraints, i.e., supply the scalar you'd like to constrain as an outcome of your experiment, and then set constraints on that scalar's value via outcome constraints (pointer). Hope that helps!

@bernardbeckerman thanks for your answer . how can I convert the constarined to scaler and it is depend on the value of x1,x2 etc. which has range between two values? as wellas I have 3 constatins not just the above one?

[bernardbeckerman]

Following the example in this tutorial, you can do this by adding an outcome_constraint to the ax_client.create_experiment call, e.g.,

ax_client.create_experiment(
    ...
    outcome_constraints=["length_over_dist >= 3"]
)

and then adding the calculation of the desired quantity to the evaluate function and including it in the output dictionary, e.g.,

def evaluate(parameterization):
    x = np.array([parameterization.get(f"x{i+1}") for i in range(6)])
    dist = 100 / x4
    length = ((x2 / np.tan(np.radians(x3))) / (x11 * math.pi) * 100
    # In our case, standard error is 0, since we are computing a synthetic function.
    return {
        "hartmann6": (hartmann6(x), 0.0), 
        "l2norm": (np.sqrt((x**2).sum()), 0.0), 
        "length_over_dist": (length/dist, 0.0)
    }

Hope that helps!

[Fa20]

Does this the same like if we set the constrained on parameters?does this mean that the suggested values of parameters will satisfy this condition. Because I need the values of the parameters to simulate the objective functions after each trial in order to get the value of the objective functions

[Fa20]

and I have MOO problem

[bernardbeckerman]

If you follow my recommendation here (link) to avoid computation of the objective(s) if the (nonlinear) constraint is violated, then any trial with status COMPLETED should satisfy the constraint. This should work with constrained or non-constrained optimization. Does that make sense?

[bernardbeckerman]

@Fa20 have you had luck with the above suggestion? Closing this out for now but please feel free to comment/reopen with any further questions.

[Fa20]

ok thank you very much