miMLHD {MOLHD}

Generate the optimal Latin Hypercube Design based on the miniMax criterion.

Description

Generate the optimal Latin Hypercube Design based on the miniMax criterion.

Usage

miMLHD(n, p, num = 50, temp0 = 0, nstarts = 1, times = 300,
  maxiter = 1e+06)

Arguments

n

number of runs desired

p

number of variables desired

num

Optional, default is "50". The fineness of the gridded points to divide the design space. Each dimension is evenly divided by num+1 points. Lower this parameter when dimension is high to reduce computing time.

temp0

Initial temperature for simulated annealing

nstarts

Optional, default is "1". The number of random starts

times

Optional, default is "300". The maximum number of non-improving searches allowed before terminating the search.

maxiter

Optional, default is "1e+06".The maximum total number of iterations for each random start. Lower this number if the design is prohibitively large and you want to terminate the algorithm prematurely to report the best design found

Details

This function is to search the optimal Latin Hypercube design based on the miniMax criterion using the columnwise exchange algorithm coupled with the simulated annealing algorithm, and several computational shortcuts to improve efficiency. The approximate miniMax criterion is computed by using a set of gridded points to approximate the continuous design space, the maximum error of the value can be computed.(Can only work in relatively low dimensions)

Value

design

The optimal miniMax design matrix

criterion

The opproximate miniMax criterion for the chosen fineness of the grids

iterations

The total iterations

time_rec

Time to complete the search

Examples

#Generate the optimal minimax distance LHD(20,2)
## Not run: 
D=miMLHD(n=20,p=2)
D$design
D$criterion

## End(Not run)