Broyden's method

In numerical analysis, Broyden's method is a quasi-Newton method for finding roots in variables. It was originally described by C. G. Broyden in 1965.

Newton's method for solving uses the Jacobian matrix, , at every iteration. However, computing this Jacobian is a difficult and expensive operation. The idea behind Broyden's method is to compute the whole Jacobian only at the first iteration and to do rank-one updates at other iterations.

In 1979 Gay proved that when Broyden's method is applied to a linear system of size , it
terminates in steps, although like all quasi-Newton methods, it may not converge for nonlinear systems.

Description of the method

Solving single-variable equation

In the secant method, we replace the first derivative at with the finite-difference approximation:

:$f'(x_n) \simeq \frac,$

and proceed similar to Newton's method:

:$x_ = x_n - \frac$

where is the iteration index.

Solving a system of nonlinear equations

Consider a system of nonlinear equations
:$\mathbf f(\mathbf x) = \mathbf 0 ,$

where is a vector-valued function of vector :

:$\mathbf x = (x_1, x_2, x_3, \dotsc, x_k),$
:$\mathbf f(\mathbf x) = \big(f_1(x_1, x_2, \dotsc, x_k), f_2(x_1, x_2, \dotsc, x_k), \dotsc, f_k(x_1, x_2, \dotsc, x_k)\big).$

For such problems, Broyden gives a generalization of the one-dimensional Newton's method, replacing the derivative with the Jacobian . The Jacobian matrix is determined iteratively, based on the secant equation in the finite-difference approximation:

:$\mathbf J_n (\mathbf x_n - \mathbf x_) \simeq \mathbf f(\mathbf x_n) - \mathbf f(\mathbf x_),$

where is the iteration index. For clarity, let us define:

:$\mathbf f_n = \mathbf f(\mathbf x_n),$
:$\Delta \mathbf x_n = \mathbf x_n - \mathbf x_,$
:$\Delta \mathbf f_n = \mathbf f_n - \mathbf f_,$

so the above may be rewritten as

:$\mathbf J_n \Delta \mathbf x_n \simeq \Delta \mathbf f_n.$

The above equation is underdetermined when is greater than one. Broyden suggests using the current estimate of the Jacobian matrix and improving upon it by taking the solution to the secant equation that is a minimal modification to :

:$\mathbf J_n = \mathbf J_ + \frac \Delta \mathbf x_n^.$

This minimizes the following Frobenius norm:

:$\, \mathbf J_n - \mathbf J_\, _ .$

We may then proceed in the Newton direction:

:$\mathbf x_ = \mathbf x_n - \mathbf J_n^ \mathbf f(\mathbf x_n) .$

Broyden also suggested using the Sherman–Morrison formula to update directly the inverse of the Jacobian matrix:

:$\mathbf J_n^ = \mathbf J_^ + \frac \Delta \mathbf x_n^ \mathbf J^_.$

This first method is commonly known as the "good Broyden's method".

A similar technique can be derived by using a slightly different modification to . This yields a second method, the so-called "bad Broyden's method" (but see):
:$\mathbf J_n^ = \mathbf J_^ + \frac \Delta \mathbf f_n^.$

This minimizes a different Frobenius norm:

:$\, \mathbf J_n^ - \mathbf J_^\, _.$

Many other quasi-Newton schemes have been suggested in optimization, where one seeks a maximum or minimum by finding the root of the first derivative (gradient in multiple dimensions). The Jacobian of the gradient is called Hessian and is symmetric, adding further constraints to its update.

Other members of the Broyden class

Broyden has defined not only two methods, but a whole class of methods. Other members of this class have been added by other authors.
* The Davidon–Fletcher–Powell update is the only member of this class being published before the two members defined by Broyden.
* Schubert's or sparse Broyden algorithm – a modification for sparse Jacobian matrices.
* Klement (2014) – uses fewer iterations to solve many equation systems.

See also

* Secant method
* Newton's method
* Quasi-Newton method
* Newton's method in optimization
* Davidon–Fletcher–Powell formula
* Broyden–Fletcher–Goldfarb–Shanno (BFGS) method

References

Further reading

*
*

External links

Simple basic explanation: The story of the blind archer

{{Optimization algorithms, convex

Quasi-Newton methods