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Optimization and Fit in SciPy – scipy.optimize

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))
#use BFGS algorithm for optimization
optimize.fmin_bfgs(function, 0)


Optimization terminated successfully.

Current function value: -23.241676

Iterations: 4

Function evaluations: 18

Gradient evaluations: 6


  • In this example, optimization is done with the help of the gradient descent algorithm from the initial point
  • But the possible issue is local minima instead of global minima. If we don’t find a neighbor of global minima, then we need to apply global optimization and find global minima function used as basinhopping() which combines local optimizer.

optimize.basinhopping(function, 0)


fun: -23.241676238045315
      fun: -23.241676238045315
 hess_inv: array([[0.05023331]])
      jac: array([4.76837158e-07])
  message: 'Optimization terminated successfully.'
     nfev: 15
      nit: 3
     njev: 5
   status: 0
  success: True
        x: array([-1.67096375])
                    message: ['requested number of basinhopping iterations completed successfully']
      minimization_failures: 0
                       nfev: 1530
                        nit: 100
                       njev: 510
               x: array([-1.67096375])


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