twBinOptimize: twBinOptimize

In twMisc: Utility functions of tw

Description

Finding the index of a sorted numeric vector whose element is closest to target.

Usage

twBinOptimize(x, target = 0, ..., interval = c(1, length(x)), 
    lower = ceiling(min(interval)), upper = floor(max(interval)), 
    maxiter = 100, showiter = FALSE)

Arguments

x	numeric vector to be optimized. The term abs(x[i])-target) is minimized.
target	Target value for x. Defaults to 0.
...	not used
interval	a vector containing the end-points of the interval to be searched for target.
lower	the lower end point of the interval to be searched.
upper	the upper end point of the interval to be searched.
maxiter	Maximum number of search iterations. Defaults to 100.
showiter	Boolean flag indicating whether the algorithm state should be printed at each iteration. Defaults to FALSE.

Details

This function can be applied similar to optimize for cases, where the argument to be optimized is an index instead of a continuous variable. A binary search over the index is performed.
The function x must be monotonic. If it is not strictly monotonic the returned index ($where) can be any of the indices with equal values.
The code is based on binsearch of the gtools package. In difference to the original code, it returns always only one best estimate.

Value

A list containing:

call	How the function was called.
numiter	The number of iterations performed
flag	One of the strings, "Found", "Between Elements", "Maximum number of iterations reached", "Reached lower boundary", or "Reached upper boundary."
where	One or two values indicating where the search terminated.
value	Value of the x at the index of where.

If vector is empty or upper boundary is lower than lower boundary, where and value are NA

Author(s)

Thomas Wutzler

See Also

twBinOptimizeFun, twMisc

Examples

# linear search is faster up with vectors to about 1e4 entries 
x <- exp(seq(-10,100,length.out=1e4))
# with longer vectors, the binary search is superior
x <- exp(seq(-10,100,length.out=1e6))

# generate some sample indices that will be found by optimization
n <- 1e2
.where <- sample.int(length(x),n, replace=TRUE)
.val <- x[.where]*1.00001   # add some error, for exact matches use the faster function match
system.time( .where2 <- sapply(.val, function(vali){ which.min(abs(x-vali))  } ))   
system.time( .where3 <- sapply(.val, function(vali){ twBinOptimize(x,vali)$where} ))
#Rprof();  .numiter3 <- sapply(.val, function(vali){ twBinOptimize(x,vali)$numiter} ); Rprof(NULL); summaryRprof()

twMisc documentation built on May 2, 2019, 6:11 p.m.