# scipy.optimize.BroydenFirst#

class scipy.optimize.BroydenFirst(alpha=None, reduction_method='restart', max_rank=None)[source]#

Find a root of a function, using Broyden’s first Jacobian approximation.

This method is also known as "Broyden’s good method".

Parameters:
%(params_basic)s
%(broyden_params)s
%(params_extra)s

root

Interface to root finding algorithms for multivariate functions. See method='broyden1' in particular.

Notes

This algorithm implements the inverse Jacobian Quasi-Newton update

$H_+ = H + (dx - H df) dx^\dagger H / ( dx^\dagger H df)$

which corresponds to Broyden’s first Jacobian update

$J_+ = J + (df - J dx) dx^\dagger / dx^\dagger dx$

References

[1]

B.A. van der Rotten, PhD thesis, "A limited memory Broyden method to solve high-dimensional systems of nonlinear equations". Mathematisch Instituut, Universiteit Leiden, The Netherlands (2003).

https://web.archive.org/web/20161022015821/http://www.math.leidenuniv.nl/scripties/Rotten.pdf

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.broyden1(fun, [0, 0])
>>> sol
array([0.84116396, 0.15883641])


Methods

 aspreconditioner matvec rmatvec rsolve setup solve todense update