data.extrema.2: Compute Data Extema

In mpoll/scale: The Scaleable Langevin Exact Algorithm -- Reference Implementation

Usage

data.extrema.2(design.min, design.max)

Arguments

design.min
design.max

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (design.min, design.max) 
{
    extrema.design <- permutations(n = 2, r = dimen, v = c(0, 
        1), repeats.allowed = TRUE)
    for (i in 1:dimen) {
        extrema.design[, i] <- (1 - extrema.design[, i]) * design.min[i] + 
            extrema.design[, i] * design.max[i]
    }
    extrema.design <- rbind(extrema.design, extrema.design)
    data.extrema <- matrix(c(rep(0, dim(extrema.design)[1]/2), 
        rep(1, dim(extrema.design)[1]/2)), dim(extrema.design)[1], 
        1)
    data.precompute <- array(0, c(dim(extrema.design)[1], dimen, 
        dimen, 2))
    for (i in 1:dim(extrema.design)[1]) {
        for (dim.j in 1:dimen) {
            X.row <- extrema.design[i, ]
            data.precompute[i, 1:dimen, dim.j, 1] <- abs(-n.sigma[dim.j, 
                dim.j] * diag(n.sigma) * X.row[dim.j] * X.row)/4
            data.precompute[i, 1:dimen, dim.j, 2] <- abs(-(n.sigma[dim.j, 
                dim.j])^2 * diag(n.sigma) * (X.row[dim.j])^2 * 
                X.row) * 1/(6 * sqrt(3))
        }
    }
    dim.grad.max <- array(0, c(dimen, dimen, 2))
    for (dim.j in 1:dimen) {
        for (dim.k in 1:dimen) {
            dim.grad.max[dim.j, dim.k, 1] <- max(data.precompute[, 
                dim.j, dim.k, 1])
            dim.grad.max[dim.j, dim.k, 2] <- max(data.precompute[, 
                dim.j, dim.k, 2])
        }
    }
    dim.grad.max <<- dim.grad.max
    list(design.min = design.min, design.max = design.max, dim.grad.max = dim.grad.max)
  }

mpoll/scale documentation built on Dec. 9, 2019, 7:15 a.m.