Rmpfr: optimizeR – R documentation

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optimizeR

High Precision One-Dimensional Optimization

Description

optimizeR searches the interval from lower to upper for a minimum of the function f with respect to its first argument.

Usage

optimizeR(f, lower, upper, ..., tol = 1e-20,
          method = c("Brent", "GoldenRatio"),
          maximum = FALSE,
          precFactor = 2.0, precBits = -log2(tol) * precFactor,
          maxiter = 1000, trace = FALSE)

Arguments

`f`	the function to be optimized. `f(x)` must work “in Rmpfr arithmetic” for `optimizer()` to make sense. The function is either minimized or maximized over its first argument depending on the value of `maximum`.
`...`	additional named or unnamed arguments to be passed to `f`.
`lower`	the lower end point of the interval to be searched.
`upper`	the upper end point of the interval to be searched.
`tol`	the desired accuracy, typically higher than double precision, i.e., `tol < 2e-16`.
`method`	`character` string specifying the optimization method.

`maximum`	logical indicating if f() should be maximized or minimized (the default).
`precFactor`	only for default `precBits` construction: a factor to multiply with the number of bits directly needed for `tol`.
`precBits`	number of bits to be used for `mpfr` numbers used internally.
`maxiter`	maximal number of iterations to be used.
`trace`	integer or logical indicating if and how iterations should be monitored; if an integer k, print every k-th iteration.

Details

"Brent":

Brent(1973)'s simple and robust algorithm is a hybrid, using a combination of the golden ratio and local quadratic (“parabolic”) interpolation. This is the same algorithm as standard R's optimize(), adapted to high precision numbers.

In smooth cases, the convergence is considerably faster than the golden section or Fibonacci ratio algorithms.

"GoldenRatio":

The golden ratio method, aka ‘golden-section search’ works as follows: from a given interval containing the solution, it constructs the next point in the golden ratio between the interval boundaries.

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; iter specifiying the number of iterations, the logical convergence indicating if the iterations converged and estim.prec which is an estimate or an upper bound of the final precision (in x). method the string of the method used.

Author(s)

"GoldenRatio" is based on Hans Werner Borchers' golden_ratio (package pracma); modifications and "Brent" by Martin Maechler.

Examples

## The minimum of the Gamma (and lgamma) function (for x > 0):
Gmin <- optimizeR(gamma, .1, 3, tol = 1e-50)
str(Gmin, digits = 8)
## high precision chosen for "objective"; minimum has "estim.prec" = 1.79e-50
Gmin[c("minimum","objective")]
## it is however more accurate to 59 digits:
asNumeric(optimizeR(gamma, 1, 2, tol = 1e-100)$minimum - Gmin$minimum)


iG5 <- function(x) -exp(-(x-5)^2/2)
curve(iG5, 0, 10, 200)
o.dp  <- optimize (iG5, c(0, 10)) #->  5 of course
oM.gs <- optimizeR(iG5, 0, 10, method="Golden")
oM.Br <- optimizeR(iG5, 0, 10, method="Brent", trace=TRUE)
oM.gs$min ; oM.gs$iter
oM.Br$min ; oM.Br$iter
(doExtras <- Rmpfr:::doExtras())
if(doExtras) {## more accuracy {takes a few seconds}
 oM.gs <- optimizeR(iG5, 0, 10, method="Golden", tol = 1e-70)
 oM.Br <- optimizeR(iG5, 0, 10,                  tol = 1e-70)
}
rbind(Golden = c(err = as.numeric(oM.gs$min -5), iter = oM.gs$iter),
      Brent  = c(err = as.numeric(oM.Br$min -5), iter = oM.Br$iter))

## ==> Brent is orders of magnitude more efficient !

## Testing on the sine curve with 40 correct digits:
sol <- optimizeR(sin, 2, 6, tol = 1e-40)
str(sol)
sol <- optimizeR(sin, 2, 6, tol = 1e-50,
                 precFactor = 3.0, trace = TRUE)
pi.. <- 2*sol$min/3
print(pi.., digits=51)
stopifnot(all.equal(pi.., Const("pi", 256), tolerance = 10*1e-50))

if(doExtras) { # considerably more expensive

## a harder one:
f.sq <- function(x) sin(x-2)^4 + sqrt(pmax(0,(x-1)*(x-4)))*(x-2)^2
curve(f.sq, 0, 4.5, n=1000)
msq <- optimizeR(f.sq, 0, 5, tol = 1e-50, trace=5)
str(msq) # ok
stopifnot(abs(msq$minimum - 2) < 1e-49)

## find the other local minimum: -- non-smooth ==> Golden ratio -section is used
msq2 <- optimizeR(f.sq, 3.5, 5, tol = 1e-50, trace=10)
stopifnot(abs(msq2$minimum - 4) < 1e-49)

## and a local maximum:
msq3 <- optimizeR(f.sq, 3, 4, maximum=TRUE, trace=2)
stopifnot(abs(msq3$maximum - 3.57) < 1e-2)

}#end {doExtras}


##----- "impossible" one to get precisely ------------------------

ff <- function(x) exp(-1/(x-8)^2)
curve(exp(-1/(x-8)^2), -3, 13, n=1001)
(opt. <- optimizeR(function(x) exp(-1/(x-8)^2), -3, 13, trace = 5))
## -> close to 8 {but not very close!}
ff(opt.$minimum) # gives 0
if(doExtras) {
 ## try harder ... in vain ..
 str(opt1 <- optimizeR(ff, -3,13, tol = 1e-60, precFactor = 4))
 print(opt1$minimum, digits=20)
 ## still just  7.99998038 or 8.000036655 {depending on method}
}

Rmpfr

R MPFR - Multiple Precision Floating-Point Reliable

v0.8-4

GPL (>= 2)

Authors

Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>), Richard M. Heiberger [ctb] (formatHex(), *Bin, *Dec), John C. Nash [ctb] (hjkMpfr(), origin of unirootR()), Hans W. Borchers [ctb] (optimizeR(*, "GoldenRatio"); origin of hjkMpfr())

Initial release

2021-04-08