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import numpy as np
from gradient_free_optimizers import RandomSearchOptimizer
def convex_function(pos_new):
score = -(pos_new["x1"] * pos_new["x1"] + pos_new["x2"] * pos_new["x2"])
return score
search_space = {
"x1": np.arange(-100, 101, 0.1),
"x2": np.arange(-100, 101, 0.1),
}
def constraint_1(para):
# only values in 'x1' higher than -5 are valid
return para["x1"] > -5
# put one or more constraints inside a list
constraints_list = [constraint_1]
# pass list of constraints to the optimizer
opt = RandomSearchOptimizer(search_space, constraints=constraints_list)
opt.search(convex_function, n_iter=50)
search_data = opt.search_data
# the search-data does not contain any samples where x1 is equal or below -5
print("\n search_data \n", search_data, "\n")
SimonBlanke /
Gradient-Free-Optimizers
