correctionCNSD: Correcting Conditional Negative Semi-Definiteness

In CEGO: Combinatorial Efficient Global Optimization

Correcting Conditional Negative Semi-Definiteness

Description

Correcting, e.g., a distance matrix with chosen methods so that it becomes a CNSD matrix.

Usage

correctionCNSD(mat, method = "flip", tol = 1e-08)

Arguments

mat	symmetric matrix, which should be at least of size 3x3
method	string that specifies method for correction: spectrum clip "clip", spectrum flip "flip", nearest definite matrix "near", spectrum square"square", spectrum diffusion "diffusion".
tol	torelance value. Eigenvalues between -tol and tol are assumed to be zero.

Value

the corrected CNSD matrix

References

Martin Zaefferer and Thomas Bartz-Beielstein. (2016). Efficient Global Optimization with Indefinite Kernels. Parallel Problem Solving from Nature-PPSN XIV. Accepted, in press. Springer.

See Also

modelKriging

Examples

x <- list(c(2,1,4,3),c(2,4,3,1),c(4,2,1,3),c(4,3,2,1),c(1,4,3,2))
D <- distanceMatrix(x,distancePermutationInsert)
is.CNSD(D) #matrix should not be CNSD
D <- correctionCNSD(D)
is.CNSD(D) #matrix should now be CNSD
D
# note: to fix the negative distances, use repairConditionsDistanceMatrix. 
# Or else, use correctionDistanceMatrix.

CEGO documentation built on May 29, 2024, 3:35 a.m.