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# scipy.optimize.diagbroyden

`scipy.optimize.``diagbroyden`(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 diagonal Broyden Jacobian approximation.

  The Jacobian approximation is derived from previous iterations, by retaining only the diagonal of Broyden matrices.

  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 array-like object. x0 : array_like Initial guess for the solution alpha : float, optional Initial guess for the Jacobian 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 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.

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