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Parameters

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

%(params_basic)s :
alpha : float, optional

Initial guess for the Jacobian is (-1/alpha).

%(params_extra)s :

Find a root of a function, using diagonal Broyden Jacobian approximation.

See Also

root

Interface to root finding algorithms for multivariate functions. See method=='diagbroyden' in particular.

Examples

The following functions define a system of nonlinear equations

>>> def fun(x):
...  return [x[0] + 0.5 * (x[0] - x[1])**3 - 1.0,
...  0.5 * (x[1] - x[0])**3 + x[1]]

A solution can be obtained as follows.

>>> from scipy import optimize
... sol = optimize.diagbroyden(fun, [0, 0])
... sol array([0.84116403, 0.15883384])
See :

Local connectivity graph

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GitHub : /scipy/optimize/_nonlin.py#1135
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