scalar_minimize

SeparableModelResult.scalar_minimize(method='Nelder-Mead', params=None, **kws)

Scalar minimization using scipy.optimize.minimize.

Perform fit with any of the scalar minimization algorithms supported by scipy.optimize.minimize. Default argument values are:

scalar_minimize() arg Default Value Description
method Nelder-Mead fitting method
tol 1.e-7 fitting and parameter tolerance
hess None Hessian of objective function
Parameters:
  • method (str, optional) –

    Name of the fitting method to use. One of:

    • ’Nelder-Mead’ (default)
    • ’L-BFGS-B’
    • ’Powell’
    • ’CG’
    • ’Newton-CG’
    • ’COBYLA’
    • ’BFGS’
    • ’TNC’
    • ’trust-ncg’
    • ’trust-exact’ (SciPy >= 1.0)
    • ’trust-krylov’ (SciPy >= 1.0)
    • ’trust-constr’ (SciPy >= 1.1)
    • ’dogleg’
    • ’SLSQP’
    • ’differential_evolution’
  • params (Parameters, optional) – Parameters to use as starting point.
  • **kws (dict, optional) – Minimizer options pass to scipy.optimize.minimize.
Returns:

Object containing the optimized parameter and several goodness-of-fit statistics.
Return type:

MinimizerResult

Changed in version 0.9.0: Return value changed to MinimizerResult.

Notes

If the objective function returns a NumPy array instead of the expected scalar, the sum of squares of the array will be used.

Note that bounds and constraints can be set on Parameters for any of these methods, so are not supported separately for those designed to use bounds. However, if you use the differential_evolution method you must specify finite (min, max) for each varying Parameter.