Optimization and Fit in SciPy – scipy.optimize Optimization provides a useful algorithm for minimization of curve fitting, multidimensional or scalar and root fitting. Let’s take an example of a Scalar Function, to find minimum scalar function %matplotlib inline import matplotlib.pyplot as plt from scipy import optimize import numpy as np def function(a): return a*2 + 20 * np.sin(a) plt.plot(a, function(a)) plt.show() #use BFGS algorithm for optimization optimize.fmin_bfgs(function, 0) Output: Optimization terminated successfully. Current function value: -23.241676 Iterations: 4 Function evaluations: 18 Gradient evaluations: 6 array([-1.67096375]) In this example, optimization is done with the help of the gradient descent algorithm from the initial…