Having trouble with scipy Minimize function, it is giving me odd results

Question

Created an objective function Added constraints The problem is no matter what initial guess I use, the minimize functions just keeps on using that number. for example: If I use 15 for the initial guess, the solver will not try any other number and say the answer is 15. I'm sure the ere is an issue with the code but I am not sure where. CODE BELOW: ``` from scipy.optimize import minimize import numpy as np from pandas import * #---------------------------------------------------- #-------- Create Function ------------ #---------------------------------------------------- def MovingAverage(Input,N,test=0): # Create data frame df = DataFrame(Input, columns=['Revenue']) # Add columns df['CummSum'] = df['Revenue'].cumsum() df['Mavg'] = rolling_mean(df['Revenue'], N) df['Error'] = df['Revenue'] - df['Mavg'] df['MFE'] = (df['Error']).mean() df['MAD'] = np.fabs(df['Error']).mean() df['MSE'] = np.sqrt(np.square(df['Error']).mean()) df['TS'] = np.sum(df['Error'])/df['MAD'] print N, df.MAD[0] if…

Accepted Answer

Your function is piecewise constant between integer input values, as seen in the plot below (plotted in steps of 0.1 on the x axis):

So the derivative is zero at almost all points, and that's why a gradient based minimization method will return any given initial point as a local minimum.

To rescue the situation, you could think about using interpolation in the objective function to get intermediate function values for non-integer input values. If you combine this with a gradient-based minimization, it might find some point around 8 as a local minimum when starting at 15.

Having trouble with scipy Minimize function, it is giving me odd results

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