Nothing
`TotalVar` <-
function(x,DiscretePrevision,n,s){
x.nodevalues <- DiscretePrevision[[1]]
omega.nodevalues <- DiscretePrevision[[2]]
UpperPrevisions <- DiscretePrevision[[3]]
m <- ncol(omega.nodevalues) # Number of additional supporting nodes
# Loosing some more or less unneccessary nodes gives:
ListRedNnodev <- FctReducedNodevalues(n,m,s,x.nodevalues,omega.nodevalues)
x.nodevalues <- ListRedNnodev[[1]]
x.frequency <- ListRedNnodev[[2]]
omega.nodevalues <- ListRedNnodev[[3]]
mr <- ListRedNnodev[[4]]
# The number of observed nodes (which may differ from n now)
nr <- length(x.frequency) # 'nr' appreviates: n-reduced
# Updating the number of additional supporting nodes
m <- mr
# all values of the functions f at all remaining nodes
nodevalues <- cbind(x.nodevalues,omega.nodevalues)
# Buiding the linear function 'a' which has to be optimized:
a <- BuildOptVec(nr,m)
# Building the matrix A:
A0 <- BuildMatrix1(nodevalues,nr)
A1234 <- BuildMatrix2(nr,m)
A <- BuildMatrix(A0,A1234)
# Building the bounds b:
b0 <- BuildBounds1(UpperPrevisions)
b1234 <- BuildBounds2(n,nr,x.frequency,m)
b <- BuildBounds(b0,b1234)
# Solving the maximization problem by use of LpSolve:
library(lpSolve)
l <- rep("<=",length(A[,1]))
ergebnis <- lp(direction="max", objective.in=a, const.mat=A, const.dir=l, const.rhs=b)
list(2*(1-ergebnis$objval), ergebnis$status)
}
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