def func(x): return x**2 + 10*np.sin(x)
res = minimize(func, x0=1.0) print(res.x) import numpy as np from scipy.interpolate import interp1d numerical recipes python pdf
def invert_matrix(A): return np.linalg.inv(A) def func(x): return x**2 + 10*np
f = interp1d(x, y, kind='cubic') x_new = np.linspace(0, 10, 101) y_new = f(x_new) kind='cubic') x_new = np.linspace(0
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A = np.array([[1, 2], [3, 4]]) A_inv = invert_matrix(A) print(A_inv) import numpy as np from scipy.optimize import minimize