`scipy.optimize._nonlin:nonlin_solve`

source: /scipy/optimize/_nonlin.py :125

Signature

   def     nonlin_solve (    F    ,    x0    ,    jacobian    =  krylov   ,    iter    =  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   ,    full_output    =  False   ,    raise_exception    =  True     )

Summary

Find a root of a function, in a way suitable for large-scale problems.

Parameters

F : function(x) -> f: Function whose root to find; should take and return an array-like object.
xin : array_like: Initial guess for the solution
jacobian : Jacobian: A Jacobian approximation: Jacobian object or something that asjacobian can transform to one. Alternatively, a string specifying which of the builtin Jacobian approximations to use:
krylov, broyden1, broyden2, anderson diagbroyden, linearmixing, excitingmixing
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 max-norm) for the residual. If omitted, default is 6e-6.
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.
full_output : bool: If true, returns a dictionary info containing convergence information.
raise_exception : bool: If True, a NoConvergence exception is raise if no solution is found.

Notes

This algorithm implements the inexact Newton method, with backtracking or full line searches. Several Jacobian approximations are available, including Krylov and Quasi-Newton methods.

Aliases

scipy.optimize._nonlin.nonlin_solve