#330 Gradient method using python

golden.py

import math as mt
def golden(f,a,b,tol=1.0e-10):
    # Golden section method for determining x
    # that minimizes the scalar function f(x).
    # The minimum must be bracketed in (a,b).
    c1 = (mt.sqrt(5.0)-1.0)/2.0
    c2 = 1.0 - c1
    nIt = int(mt.ceil(mt.log(tol/abs(a-b))/mt.log(c1)))
    # first step
    x1 = c1*a + c2*b
    x2 = c2*a + c1*b
    f1=f(x1)
    f2=f(x2)
    # iteration
    for i in range(nIt):
        if (f1 > f2):
            a = x1
            x1 = x2
            f1 = f2
            x2 = c2*a + c1*b
            f2=f(x2)
        else:
            b = x2
            x2 = x1
            f2 = f1
            x1 = c1*a + c2*b
            f1 = f(x1)
    if (f1<f2):
        return x1,f1
    else:
        return x2,f2

grad.py

import numpy as np
import math as mt
from golden import golden

def grad(F,gradf,x,d=0.5,tol=1.0e-10):
    # Gradient method for determining vector x
    # that minimizes the function F(x);
    # gradf(x) is function for grad(F);
    # x is the starting point.
    # Line function along h
    def f(al):
        return F(x+al*h)
    gr0=-gradf(x)
    h = gr0.copy()
    F0=F(x)
    itMax = 500
    for i in range(itMax):
        # Minimization 1D function
        al,fMin = golden(f,0,d)
        x = x + al*h
        F1=F(x)
        gr1=-gradf(x)
        if (mt.sqrt(np.dot(gr1,gr1)) <= tol) or (abs(F0 - F1) < tol):
            return x, i+1
        h = gr1
        grO = gr1.copy()
        F0 = F1
    
    print("Gradient method did not converge (500 iterations)")

ex330.py

import numpy as np
import matplotlib.pyplot as plt
from grad import grad

def F(x):
    return 10.0*(x[1]-x[0]**2)**2 + (1-x[0])**2

def gradf(x):
    gr = np.zeros((2),'float')
    gr[0] = -40.0*(x[1]-x[0]**2) * x[0] - 2.0*(1-x[0])
    gr[1] = 20.0*(x[1]-x[0]**2)
    return gr

x = np.linspace(-3.0,3.0,101)
y = np.linspace(-2.0,6.0,101)
X,Y = np.meshgrid(x,y)
z=F([X,Y])
v=np.linspace(0.0,20.0,5)
plt.contourf(x,y,z,v, cmap=plt.cm.gray)
plt.colorbar()
x0=np.array([0.0,0.1])
xMin,nIt=grad(F,gradf,x0)
print("xMin=",xMin)
print("Number of iterations=",nIt)
plt.scatter(xMin[0],xMin[1],c="red")
plt.savefig('ex330.png', dpi=72)
plt.show()

Last Updated on 2023-01-03 by gantovnik