inter_min.py

import numpy as np

np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;

pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = False
import pylab as plt

from scipy.optimize import minimize


def inter_min(x,inter_par, Ain=[], bin=[]):
    # %find the minimizer of the interpolating function starting with x
    # rho = 0.9 # backtracking paramtere
    n = x.shape[0]
#     start the serafh method
    iter = 0
    x0 = np.zeros((n, 1))
    # while iter < 10:
    H = np.zeros((n, n))
    g = np.zeros((n, 1))
    y = interpolate_val(x, inter_par)
    g = interpolate_grad(x, inter_par)
    # H = interpolate_hessian(x, inter_par)
    #     Perform the Hessian modification
    # H = modichol(H, 0.01, 20);
    # H = (H + H.T)/2.0
#         optimizaiton for finding hte right direction
    objfun3 = lambda x: (interpolate_val(x, inter_par))
    grad_objfun3 = lambda x:  interpolate_grad(x, inter_par)
    res = minimize(objfun3, x0, method='L-BFGS-B', jac=grad_objfun3, options={'gtol': 1e-6, 'disp': True})
    return res.x, res.fun




xi = np.array([[0.5000 , 0.8000   , 0.5000,    0.2000,    0.5000],  [0.5000,    0.5000,    0.8000,    0.5000,    0.2000]])
x0 = np.array([[0.5], [0.53]]);
yi=fun(xi)
inter_par = Inter_par()
inter_par= interpolateparameterization(xi, yi, inter_par)
# g0 = interpolate_grad(x0,inter_par)


[xm,ym] = inter_min(x0,inter_par)

# function[x
# y]=inter_min(x, inter_par)
# % find
# the
# minimizer
# of
# the
# interpolating
# function
# starting
# with x
#     global n
#     Ain
#     bin
#
# rho = 0.9; % parameters
# of
# backtracking
#
# % start
# the
# search
# with method
#     iter = 1;
#
# while iter < 10
#
# % Calculate
# the
# Newton
# direction
# H = zeros(n, n);
# g = zeros(n, 1);
# y = interpolate_val(x, inter_par);
# g = interpolate_grad(x, inter_par);
# H = interpolate_hessian(x, inter_par);
# % Perform
# the
# Hessian
# modification
# H = modichol(H, 0.01, 20);
# H = (H + H.
# ')/2;
# options = optimoptions('quadprog', 'Display', 'none');
# p = quadprog(double(H), double(g), Ain, bin - Ain * x, [], [], [], [], zeros(n, 1), options);
#
# if norm(p) < 1e-5
# break
# end
# a = 1;
# % Backtracking
# while 1
#     x1 = x + a * p;
#     y1 = interpolate_val(x1, inter_par);
#     if (y - y1) > 0
#         x = x1;
#     break
#     else
#     a = a * rho;
#     if norm(a * p) < 1e-4
#         break
#     end
#     end
#
# end
# iter = iter + 1;
# end
#