root(method=’excitingmixing’)#
- scipy.optimize.root(fun, x0, args=(), method='excitingmixing', tol=None, callback=None, options={})
See also
For documentation for the rest of the parameters, see
scipy.optimize.root- Options
- nitint, optional
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
- dispbool, optional
Print status to stdout on every iteration.
- maxiterint, optional
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
- ftolfloat, optional
Relative tolerance for the residual. If omitted, not used.
- fatolfloat, optional
Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6.
- xtolfloat, optional
Relative minimum step size. If omitted, not used.
- xatolfloat, optional
Absolute minimum step size, as determined from the Jacobian approximation. If the step size is smaller than this, optimization is terminated as successful. If omitted, not used.
- tol_normfunction(vector) -> scalar, optional
Norm to use in convergence check. Default is the maximum norm.
- line_search{None, ‘armijo’ (default), ‘wolfe’}, optional
Which type of a line search to use to determine the step size in the direction given by the Jacobian approximation. Defaults to ‘armijo’.
- jac_optionsdict, optional
Options for the respective Jacobian approximation.
- alphafloat, optional
Initial Jacobian approximation is (-1/alpha).
- alphamaxfloat, optional
The entries of the diagonal Jacobian are kept in the range
[alpha, alphamax].