This is what my code looks like when simplified:
# This function returns some value depending on the index (integer)
# with which it is called.
def funct(index):
value <-- some_process[index]
# Return value for this index.
return value
where the indexes allowed are stored in a list:
# List if indexes.
x = [0,1,2,3,...,1000]
I need to find the x index that returns the minimum value for funct. I could just apply a brute force approach and loop through the full x list storing all values in new a list and then simply find the minimum with np.argmin():
list_of_values = []
for indx in x:
f_x = funct(x)
list_of_values.append(f_x)
min_value = np.argmin(list_of_values)
I've tried this and it works, but it becomes quite expensive when x has too many elements. I'm looking for a way to optimize this process.
I know that scipy.optimize has some optimization functions to find a global minimum like anneal and basin-hopping but I've failed to correctly apply them to my code.
Can these optimization tools be used when I can only call a function with an integer (the index) or do they require the function to be continuous?