optimize {stats} | R Documentation

## One Dimensional Optimization

### Description

The function `optimize` searches the interval from `lower` to `upper` for a minimum or maximum of the function `f` with respect to its first argument.

`optimise` is an alias for `optimize`.

### Usage

    optimize(f = , interval = , lower = min(interval),
            upper = max(interval), maximum = FALSE,
            tol = .Machine$double.eps^0.25, ...)
    optimise(f = , interval = , lower = min(interval),
            upper = max(interval), maximum = FALSE,
            tol = .Machine$double.eps^0.25, ...)

### Arguments

`f` | the function to be optimized. The function is either minimized or maximized over its first argument depending on the value of `maximum`.
`interval` | a vector containing the end-points of the interval to be searched for the minimum.
`lower` | the lower end point of the interval to be searched.
`upper` | the upper end point of the interval to be searched.
`maximum` | logical. Should we maximize or minimize (the default)?
`tol` | the desired accuracy.
`...` | additional arguments to `f`.

### Details

The method used is a combination of golden section search and successive parabolic interpolation. Convergence is never much slower than that for a Fibonacci search. If `f` has a continuous second derivative which is positive at the minimum (which is not at `lower` or `upper`), then convergence is superlinear, and usually of the order of about 1.324.

The function `f` is never evaluated at two points closer together than eps * |x_0| + (tol/3), where eps is approximately `sqrt(.Machine$double.eps)` and x_0 is the final abscissa `optimize()$minimum`.
If `f` is a unimodal function and the computed values of `f` are always unimodal when separated by at least eps * |x| + (tol/3), then x_0 approximates the abscissa of the global minimum of `f` on the interval `lower,upper` with an error less than eps * |x_0|+ tol.
If `f` is not unimodal, then `optimize()` may approximate a local, but perhaps non-global, minimum to the same accuracy.

The first evaluation of `f` is always at x_1 = a + (1-phi)(b-a) where `(a,b) = (lower, upper)` and phi = (sqrt 5 - 1)/2 = 0.61803.. is the golden section ratio. Almost always, the second evaluation is at x_2 = a + phi(b-a). Note that a local minimum inside [x_1,x_2] will be found as solution, even when `f` is constant in there, see the last example.

It uses a C translation of Fortran code (from Netlib) based on the Algol 60 procedure `localmin` given in the reference.

### Value

A list with components `minimum` (or `maximum`) and `objective` which give the location of the minimum (or maximum) and the value of the function at that point.

### References

Brent, R. (1973) Algorithms for Minimization without Derivatives. Englewood Cliffs N.J.: Prentice-Hall.

### See Also

`nlm`, `uniroot`.

### Examples

    f <- function (x,a) (x-a)^2
    xmin <- optimize(f, c(0, 1), tol = 0.0001, a = 1/3)
    xmin

    ## See where the function is evaluated:
    optimize(function(x) x^2*(print(x)-1), l=0, u=10)

    ## "wrong" solution with unlucky interval and piecewise constant f():
    f  <- function(x) ifelse(x > -1, ifelse(x < 4, exp(-1/abs(x - 1)), 10), 10)
    fp <- function(x) { print(x); f(x) }

    plot(f, -2,5, ylim = 0:1, col = 2)
    optimize(fp, c(-4, 20))# doesn't see the minimum
    optimize(fp, c(-7, 20))# ok

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