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

 ```1 2 3 4``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ``` ## 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.