Optimisation Using Scipy

In the following script: import numpy as np from scipy.optimize import minimise a=np.array(range(4)) b=np.array(range(4,8)) def sm(x,a,b): sm=np.zeros(1) a=a*np.exp(

Solution 1:

Your function sm appears to be unbounded. As you increase x, sm will get ever more negative, hence the fact that it is going to -inf.

Re: comment - if you want to make sm() as close to zero as possible, modify the last line in your function definition to read return abs(sm).

This minimised the absolute value of the function, bringing it close to zero.

Result for your example:

>>>opt=minimize(sm,x0,args=(a,b),method='nelder-mead',options={'xtol':1e-8,'disp':True})Optimizationterminatedsuccessfully.Current function value:0.000000Iterations:153Function evaluations:272>>>optstatus:0nfev:272success:Truefun:2.8573836630130245e-09x:array([-1.24676625,0.65786454,0.44383101,1.73177358])message:'Optimization terminated successfully.'nit:153

Solution 2:

Modifying the proposal of FuzzyDuck, I replace sm +=((b-a)**2) which return me the desired result.