Lmfit - Minimizer Does Not Accept Scipy Minimizer Keyword Arguments

I am trying to fit some model to my data with lmfit. See the MWE below: import lmfit import numpy as np def lm(params, x): slope = params['slope'] interc = params['interc'

Solution 1:

The best way to pass keyword arguments to the underlying scipy solver would be just to use

# Note: valid but will not do what you wantfitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata), xatol=0.01)
fit = fitter.minimize(method='nelder')

or

# Also: valid but will not do what you wantfitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata))
fit = fitter.minimize(method='nelder', xatol=0.01)

The main problem here is that xatol is not a valid keyword argument for the underlying solver, scipy.optimize.minimize(). Instead, you probably mean to use tol:

fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata), tol=0.01)
fit = fitter.minimize(method='nelder')

or

fitter = lmfit.Minimizer(lm_min, params, fcn_args=(x, ydata))
fit = fitter.minimize(method='nelder', tol=0.01)

Solution 2:

In a github issue I found the following solution:

fit = fitter.minimize(method='nelder', **{'options':{'xatol':4e-4}})

Update As mentioned by @dashesy, this is the same as writing:

fit = fitter.minimize(method='nelder', options={'xatol':4e-4})

This also works for other solver options.