SciPy Recipes

How to do it...

This is how we represent the solution of the Lagrange equation following, this represents the solution of the system:

import numpy as npfrom scipy.interpolate import lagrangefrom scipy.optimize import OptimizeResult, minimize_scalardef good_bracket(func, bracket):    a, c, b = bracket    return (func(a) > func(c)) and (func(b) > func(c))def parabolic_step(f, args, bracket, **options):    stop = False    funcalls = 0    niter = 0    while not stop:        niter += 1        interpolator = lagrange(np.array(bracket), f(np.array(bracket)))        funcalls += 3        a, b, c = interpolator.coeffs        d = -0.5*b/a        if np.allclose(bracket[1], d):            minima = d            stop = True        elif bracket[1] < d:            newbracket = [bracket[1], d, bracket[2]]            if good_bracket(f, newbracket): bracket = newbracket ...