Generate the maximum projection (MaxPro) Latin hypercube design for continuous factors based on a simulated annealing algorithm. If nominal, discrete numeric, or ordinal factors exist, please see the function MaxProQQ.
MaxProLHD(n, p, s=2, temp0=0, nstarts = 1, itermax = 400, total_iter = 1e+06)The number of runs (design points)
The number of input factors (variables)
Optional, default is ``2''. The parameter in defining the s-norm distance (2 corresponds to Euclidean distance)
Optional, The initial temperature in the simulated annealing algorithm. Change this value if you want to start with a higher or lower temperature
Optional, default is ``1''. The number of random starts
Optional, default is ``400''. The maximum number of non-improving searches allowed under each temperature. Lower this parameter if you want the algorithm to converge faster
Optional, default is ``1e+06''.The maximum total number of iterations. Lower this number if the design is prohibitively large and you want to terminate the algorithm prematurely to report the best design found so far
The value returned from the function is a list containing the following components:
Design matrix
Initial temperature
The MaxPro criterion measure
Time to complete the search
The total number of iterations
This function utilizes a version of the simulated annealing algorithm to efficiently generate the optimal Latin hypercube designs for continuous factors based on the MaxPro criterion. Parameters in the algorithm may need to be properly tuned to achieve global convergence. Please refer to Joseph, Gul and Ba (2015) for details.
Joseph, V. R., Gul, E., and Ba, S. (2015) "Maximum Projection Designs for Computer Experiments," Biometrika, 102, 371-380.
# NOT RUN {
obj<-MaxProLHD(n = 10, p = 4)
obj$Design
# }
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