maximin.optim {SFDesign} | R Documentation |
Optimize a design based on maximin or reciprocal distance criterion
Description
This function optimizes a design by continuous optimization based on reciprocal distance criterion. A simulated annealing step can be enabled in the end to directly optimize the maximin distance criterion.
Usage
maximin.optim(D.ini, iteration = 10, sa = FALSE, find.best.ini = FALSE)
Arguments
D.ini |
the initial design. |
iteration |
number iterations for L-BFGS-B algorithm. |
sa |
whether to use simulated annealing in the end. If sa=TRUE, continuous optimization is first used to optimize the reciprocal distance criterion and then SA is performed to optimize the maximin criterion. |
find.best.ini |
whether to generate other initial designs. If find.best.ini=TRUE, it will first find the closest full factorial design in terms of size to |
Details
maximin.optim
optimizes a design by L-BFGS-B algorithm (Liu and Nocedal 1989) based on the reciprocal distance criterion. A simulated annealing step can be enabled in the end to directly optimize the maximin distance criterion. Optimization detail can be found in continuous.optim
. We also provide the option to try other initial designs generated internally besides the D.ini
provided by the user (see argument find.best.ini
).
Value
design |
the optimized design. |
D.ini |
initial designs. If find.best.ini=TRUE, a list will be returned containing all the initial designs considered. |
References
Liu, D. C., & Nocedal, J. (1989). On the limited memory BFGS method for large scale optimization. Mathematical programming, 45(1), 503-528.
Examples
n = 20
p = 3
D = maximinLHD(n, p)$design
D = maximin.optim(D, sa=FALSE)$design
# D = maximin.optim(D, sa=TRUE)$design # Let sa=TRUE only when the n and p is not large.