dualgreedy
Dualgreedy algorithm for maximum entropy sampling
Starting point is a network A[F] with $nf$ points. Now one has to select $ns$ points of a set of candidate sites to augment the existing network. The aim of maximum entropy sampling is to select a feasible Doptimal design that maximizes the logarithm of the determinant of all principal submatrices of $A$ arising by this expansion.
It is also possible to construct a completely new network, that means $nf=0$.
This dualgreedy algorithm starts with the matrix A and deletes the worst candidate of each of the stages $(1..ns)$ to reduce this matrix.
Usage
dualgreedy(A, nf, ns, etol=0, mattest=TRUE)
Arguments
 A
 Spatial covariance matrix $A$.
 nf
 Number of stations are forced into every feasible solution.
 ns
 Number of stations have to be added to the existing network.
 etol
 Tolerance for checking positve definiteness (default 0)
 mattest
 Toggles testing matrix
A
for symmetry and positive definiteness (defaultT
)
Details
$A[F]$ denotes the principal submatrix of $A$ having rows and columns indexed by $1..nf$.
Value

A object of class
 S
 Vector containing the indices of the added sites in the solution or 0 for the other sites.
 det
 Determinant of the principal submatrix indexed by the solution.
monet
containing the following
elements:
References
Ko, Lee, Queyranne, An exact algorithm for maximum entropy sampling, Operations Research 43 (1995), 684691.
Gebhardt, C.: Bayessche Methoden in der geostatistischen Versuchsplanung. PhD Thesis, Univ. Klagenfurt, Austria, 2003
O.P. Baume, A. Gebhardt, C. Gebhardt, G.B.M. Heuvelink and J. Pilz: Network optimization algorithms and scenarios in the context of automatic mapping. Computers & Geosciences 37 (2011) 3, 289294
See Also
Examples
x < c(0.97900601,0.82658702,0.53105628,0.91420190,0.35304969,
0.14768239,0.58000004,0.60690101,0.36289026,0.82022147,
0.95290664,0.07928365,0.04833764,0.55631735,0.06427738,
0.31216689,0.43851418,0.34433556,0.77699357,0.84097327)
y < c(0.36545512,0.72144122,0.95688671,0.25422154,0.48199229,
0.43874199,0.90166634,0.60898628,0.82634713,0.29670695,
0.86879093,0.45277452,0.09386800,0.04788365,0.20557817,
0.61149264,0.94643855,0.78219937,0.53946353,0.70946842)
A < outer(x, x, "")^2 + outer(y, y, "")^2
A < (2  A)/10
diag(A) < 0
diag(A) < 1/20 + apply(A, 2, sum)
dualgreedy(A,5,5)