broyden: Broyden's Method

Description Usage Arguments Details Value Note References See Also Examples

View source: R/broyden.R

Description

Broyden's method for the numerical solution of nonlinear systems of n equations in n variables.

Usage

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broyden(Ffun, x0, J0 = NULL, ...,
        maxiter = 100, tol = .Machine$double.eps^(1/2))

Arguments

Ffun

n functions of n variables.

x0

Numeric vector of length n.

J0

Jacobian of the function at x0.

...

additional parameters passed to the function.

maxiter

Maximum number of iterations.

tol

Tolerance, relative accuracy.

Details

F as a function must return a vector of length n, and accept an n-dim. vector or column vector as input. F must not be univariate, that is n must be greater than 1.

Broyden's method computes the Jacobian and its inverse only at the first iteration, and does a rank-one update thereafter, applying the so-called Sherman-Morrison formula that computes the inverse of the sum of an invertible matrix A and the dyadic product, uv', of a column vector u and a row vector v'.

Value

List with components: zero the best root found so far, fnorm the square root of sum of squares of the values of f, and niter the number of iterations needed.

Note

Applied to a system of n linear equations it will stop in 2n steps

References

Quarteroni, A., R. Sacco, and F. Saleri (2007). Numerical Mathematics. Second Edition, Springer-Verlag, Berlin Heidelberg.

See Also

newtonsys, fsolve

Examples

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##  Example from Quarteroni & Saleri
F1 <- function(x) c(x[1]^2 + x[2]^2 - 1, sin(pi*x[1]/2) + x[2]^3)
broyden(F1, x0 = c(1, 1))
# zero: 0.4760958 -0.8793934; fnorm: 9.092626e-09; niter: 13

F <- function(x) {
    x1 <- x[1]; x2 <- x[2]; x3 <- x[3]
    as.matrix(c(x1^2 + x2^2 + x3^2 - 1,
                x1^2 + x3^2 - 0.25,
                x1^2 + x2^2 - 4*x3), ncol = 1)
}
x0 <- as.matrix(c(1, 1, 1))
broyden(F, x0)
# zero: 0.4407629 0.8660254 0.2360680; fnorm: 1.34325e-08; niter: 8

##  Find the roots of the complex function sin(z)^2 + sqrt(z) - log(z)
F2 <- function(x) {
    z  <- x[1] + x[2]*1i
    fz <- sin(z)^2 + sqrt(z) - log(z)
    c(Re(fz), Im(fz))
}
broyden(F2, c(1, 1))
# zero   0.2555197 0.8948303 , i.e.  z0 = 0.2555 + 0.8948i
# fnorm  7.284374e-10
# niter  13

##  Two more problematic examples
F3 <- function(x)
        c(2*x[1] - x[2] - exp(-x[1]), -x[1] + 2*x[2] - exp(-x[2]))
broyden(F3, c(0, 0))
# $zero   0.5671433 0.5671433   # x = exp(-x)

F4 <- function(x)   # Dennis Schnabel
        c(x[1]^2 + x[2]^2 - 2, exp(x[1] - 1) + x[2]^3 - 2)
broyden(F4, c(2.0, 0.5), maxiter = 100)

Example output

$zero
[1]  0.4760958 -0.8793934

$fnorm
[1] 9.092626e-09

$niter
[1] 13

$zero
[1] 0.4407629 0.8660254 0.2360680

$fnorm
[1] 1.34325e-08

$niter
[1] 8

$zero
[1] 0.2555197 0.8948303

$fnorm
[1] 7.28437e-10

$niter
[1] 13

$zero
[1] 0.5671433 0.5671433

$fnorm
[1] 4.677305e-12

$niter
[1] 5

$zero
[1] 1 1

$fnorm
[1] 1.18821e-10

$niter
[1] 43

pracma documentation built on Dec. 11, 2021, 9:57 a.m.