lower.bound.computational.burden: Computes a lower bound on the minimum computational burden.

In jeff-regier/MiniMiniMaxUQ: Mini-Minimax Uncertainty Quantification for Emulators

Description

Computes a lower bound on the minimum computational burden.

Usage

lower.bound.computational.burden(X, f.X, K.hat, epsilon, lp.norm = Inf)

Arguments

X	a matrix whose rows are points where f is observed
f.X	a vector whose values are f evaluated at each row of X
K.hat	the empirical Lipschitz constant of f
epsilon	the maximum uncertainty allowed
lp.norm	the L_p norm for computing the distances between points in [0, 1]^p. Defaults to sup-norm.

Examples

X <- expand.grid(1:9, 1:9) / 10
f <- function(x) sin(x[1]) + cos(x[2])
f.X <- apply(X, 1, f)
K.hat <- find.K.hat(X, f.X)
lower.bound.computational.burden(X, f.X, K.hat, epsilon=.1)

jeff-regier/MiniMiniMaxUQ documentation built on May 19, 2019, 1:45 a.m.