an optimizer
– find minimum values of functions
– build parameterized models based on data
– refine allocations to stocks in portfolios
f(x) = x^2 + x^3 + s
f(x) = (x-1.5)^2 + 0.5
"""Minimize an objective function, using SciPy."""
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as spo
def f(X):
"""Given a scalar X, return some value (a real number)."""
Y = (X - 1.5)**2 + 0.5
print "X = {}, Y = {}".format(X, Y)
return Y
def test_run():
Xguess = 2.0
min_result = spo.minimize(f, Xguess, method='SLSQP', options={'disp': True})
print "Minima found at:"
print "X = {}, Y = {}".format(min_result.x, min_result.fun)
if __name__ == "__main__":
test_run()
