scipy.optimize.linearmixing¶

scipy.optimize.
linearmixing
(F, xin, iter=None, alpha=None, verbose=False, maxiter=None, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None, tol_norm=None, line_search='armijo', callback=None, **kw)[source]¶ Find a root of a function, using a scalar Jacobian approximation.
Warning
This algorithm may be useful for specific problems, but whether it will work may depend strongly on the problem.
Parameters: F : function(x) > f
Function whose root to find; should take and return an arraylike object.
xin : array_like
Initial guess for the solution
alpha : float, optional
The Jacobian approximation is (1/alpha).
iter : int, optional
Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
verbose : bool, optional
Print status to stdout on every iteration.
maxiter : int, optional
Maximum number of iterations to make. If more are needed to meet convergence, NoConvergence is raised.
f_tol : float, optional
Absolute tolerance (in maxnorm) for the residual. If omitted, default is 6e6.
f_rtol : float, optional
Relative tolerance for the residual. If omitted, not used.
x_tol : float, 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.
x_rtol : float, optional
Relative minimum step size. If omitted, not used.
tol_norm : function(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’.
callback : function, optional
Optional callback function. It is called on every iteration as
callback(x, f)
where x is the current solution and f the corresponding residual.Returns: sol : ndarray
An array (of similar array type as x0) containing the final solution.
Raises: NoConvergence
When a solution was not found.