kmConvex1D: Kriging model with convexity 1D constraint

In maatouk/constrKriging: Kriging with inequality constraints

Kriging model with convexity 1D constraint

Usage

kmConvex1D(design, response, basis.size = dim(design)[1] + 2 + 10,
  covtype = "gauss", coef.cov = 0.5 * (max(design) - min(design)),
  coef.var = var(response), nugget = 1e-07 * sd(response))

Arguments

design	1-column matrix of the design of experiments
response	a vector containing the output values given by the real function at the design points
basis.size	a value represents the number of the basis functions (descritization of 1D input set)
covtype	an optimal character string specifying the covariance function to be used ("gauss" and "matern3_2" choice)
coef.cov	a value corresponding to the length theta hyper-parameters of covariance function
coef.var	a value specifying the variance parameter
nugget	an optimal value used as nugget effect to solve the numerical inverse matrix problem

Examples

kmConvex1D(design=c(0.1, 0.5, 0.9), response=c(10, 5, 9), coef.cov = 0.3)
kmConvex1D(design=c(0.1, 0.5,.7, 0.9), response=c(10, 5,7, 9), coef.cov = 0.3)

maatouk/constrKriging documentation built on April 24, 2024, 7:13 p.m.