# Single variable optimization

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Optimization means to seek minima or maxima of a funtion within a given defined domain.

If a function reach its maxima or minima, the derivative at that point is approaching to 0. If we apply Newton-Raphson method for root finding to *f’*, we can get the optimizing* f*.

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f2df <- function(fun) { fun.list = as.list(fun) var <- names(fun.list[1]) fun.exp = fun.list[[2]] diff.fun = D(fun.exp, var) df = list(x=0, diff.fun) df = as.function(df) return(df) } newton <- function(fun, x0, tol=1e-7, niter=100) { df = f2df(fun) for (i in 1:niter) { x = x0 - fun(x0)/df(x0) if (abs(fun(x)) < tol) return(x) x0 = x } stop("exceeded allowed number of iterations") } newton_optimize <- function(fun, x0, tol=1e-7, niter=100) { df <- f2df(fun) x = newton(df, x0, tol, niter) ddf <- f2df(df) if (ddf(x) > 0) { cat ("minima:\t", x, "\n") } else { cat ("maxima:\t", x, "\n") } return(x) } |

The golden-section method does not need *f’*. And it is similar to the root-bracketing technique for root finding.

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gSection <- function(f, x1, x2, x3, tol=1e-7) { r <- 2 - (1+sqrt(5))/2 x4 <- x2 + (x3-x2)*r if ( abs(x3-x1) < tol ){ return(x2) } if (f(x4) < f(x2)) { gSection(f, x2, x4, x3, tol) } else { gSection(f, x4, x2, x1, tol) } } |

> f newton_optimize(f, 0, tol=1e-7) minima: 0.3333333 [1] 0.3333333 > gSection(f, 0,0.5,1) [1] 0.3333333 > optimize(f, c(0,1), tol=1e-7) $minimum [1] 0.3333333 $objective [1] 0

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