BroydenFirst — SciPy v1.15.3 Manual (original) (raw)

scipy.optimize.

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

See also

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://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