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)#

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:

Ffunction(x) -> f: Function whose root to find; should take and return an array-like object.
xinarray_like: Initial guess for the solution
alphafloat, optional: The Jacobian approximation is (-1/alpha).
iterint, optional: Number of iterations to make. If omitted (default), make as many as required to meet tolerances.
verbosebool, 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.
f_tolfloat, optional: Absolute tolerance (in max-norm) for the residual. If omitted, default is 6e-6.
f_rtolfloat, optional: Relative tolerance for the residual. If omitted, not used.
x_tolfloat, 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_rtolfloat, optional: Relative minimum step size. 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’.
callbackfunction, 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:

solndarray: An array (of similar array type as x0) containing the final solution.

Raises:

NoConvergence: When a solution was not found.