# Root finding

December 4, 2010
By

(This article was first published on YGC » R, and kindly contributed to R-bloggers)

Numerical root finding methods use iteration, producing a sequence of numbers that hopefully converge towards a limits which is a root. In this post, only focus four basic algorithm on root finding, and covers bisection method, fixed point method, Newton-Raphson method, and secant method.

The simplest root finding algorithms is the bisection method. It works when f is a continuous function and it requires previous knowledge of two initial gueeses, u and v, such that f(u) and f(v) have opposite signs. This method is reliable, but converges slowly. For detail, see http://ygc.cwsurf.de/2008/11/25/bisect-to-solve-equation/ .

Root finding can be reduced to the problem of finding fixed points of the function g(x) = c*f(x) +x, where c is a non-zero constant. It is clearly that f(a) = 0 if and only if g(a) = a. This is the so called fixed point algorithm.

fixedpoint <- function(fun, x0, tol=1e-07, niter=500){
## fixed-point algorithm to find x such that fun(x) == x
## assume that fun is a function of a single variable
## x0 is the initial guess at the fixed point

xold <- x0
xnew <- fun(xold)
for (i in 1:niter) {
xold <- xnew
xnew <- fun(xold)
if ( abs((xnew-xold)) < tol )
return(xnew)
}
stop("exceeded allowed number of iterations")
}

> f <- function(x) log(x) - exp(-x)
> gfun <- function(x) x - log(x) + exp(-x)
> fixedpoint(gfun, 2)
[1] 1.309800
> x=fixedpoint(gfun, 2)
> f(x)
[1] 3.260597e-09


The fixed point algorithm is not reliable, since it cannot guaranteed to converge. Another disavantage of fixed point method is relatively slow.

Newtom-Raphson method converge more quickly than bisection method and fixed point method. It assumes the function f to have a continuous derivative. For detail, see http://ygc.cwsurf.de/2007/06/02/newton-raphson-method/ .

The secant method does not require the computation of a derivative, it only requires that the function f is continuous. The secant method is based on a linear approximation to the function f. The convergence properties of the secant method are similar to those of the Newton-Raphson method.

secant <- function(fun, x0, x1, tol=1e-07, niter=500){
for ( i in 1:niter ) {
x2 <- x1-fun(x1)*(x1-x0)/(fun(x1)-fun(x0))
if (abs(fun(x2)) < tol)
return(x2)
x0 <- x1
x1 <- x2
}
stop("exceeded allowed number of iteractions")
}
> f <- function(x) log(x) - exp(-x)
> secant(f, x0=1, x1=2)
[1] 1.309800


### Related Posts

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...