minimax.map: Compute minimax designs using clustering on a user-provided...

In minimaxdesign: Minimax and Minimax Projection Designs

Description

minimax.map computes minimax designs on a user-provided binary (0-1) image, using the minimax clustering algorithm in Mak and Joseph (2018).

Usage

minimax.map(N,img,p=2,q=10,
            params_pso=list(w=0.72,c1=1.49,c2=1.49),
            npart=5,nclust=1e5,neval=nclust,
            itmax_pso=50,itmax_pp=100,itmax_inn=1e4,jit=0.1/sqrt(N))

Arguments

N	Number of design points.
img	A binary 0-1 matrix, with 1 indicating the desired design region.
p	Dimension of design region.
q	Power parameter for approximating the minimax criterion (see paper for details). Larger values of q give a better approximation, but may cause numerical instability.
params_pso	Particle swarm optimization parameters (particle momentum (w), local-best velocity (c1) and global-best velocity (c2)).
npart	Number of particles for particle swarm optimization.
nclust,neval	Number of sample points for minimax clustering and post-processing.
itmax_pso,itmax_pp,itmax_inn	Maximum number of iterations for minimax clustering, post-processing and inner optimization.
jit	Jitter radius for post-processing.

Value

An N-by-p matrix for the minimax design.

Examples

  ## Not run: 
    #20-point minimax design on the hypercube [0,1]^2
    library(jpeg)
    n <- 25
    img <- readJPEG(system.file("img", "gamap.jpg", package="minimaxdesign"))[,,1]
    image(t(img)[,nrow(img):1],col=gray.colors(12,start=0.6),main="Georgia")
    img <- t(img)[,nrow(img):1] #Invert image due to reading distortion
    des <- minimax.map(n,img)
    points(des,pch=16)
  
## End(Not run)

minimaxdesign documentation built on July 13, 2021, 1:06 a.m.