ajd {jointDiag}

Wrapper: Joint approximate diagonalization of a set of matrices

Description

This function is mainly a wrapper to the different algorithms provided in the package. So see the help of the different algorithms for the details.

Usage

ajd(M, A0 = NULL, B0 = NULL, eps = .Machine$double.eps, 
	itermax = 200, keepTrace = FALSE, methods = c("jedi"))

Arguments

M

DOUBLE ARRAY (KxKxN). Three-dimensional array with dimensions KxKxN representing the set of square and real-valued matrices to be jointly diagonalized. N is the number of matrices. Matrices are KxK square matrices.

A0

DOUBLE MATRIX (KxK). The initial guess of the inverse of a joint diagonalizer. If NULL, an initial guess is automatically generated by the algorithm.

B0

DOUBLE MATRIX (KxK). The initial guess of a joint diagonalizer. If NULL, an initial guess is automatically generated by the algorithm.

eps

DOUBLE. The algorithm stops when the criterion difference between two iterations is less than eps.

itermax

INTEGER. Alternatively, the algorithm stops when itermax sweeps have been performed without reaching convergence. If the maximum number of iteration is performed, a warning appears.

keepTrace

BOOLEAN. Do we want to keep the successive estimations of the joint diagonalizer.

methods

STRING. One or more methods, choosen among the set of available algorithms. Possible values are: jedi, ffdiag, jadiag, uwedge, qdiag

Details

This function is mainly a wrapper to use the different algorithms provided in the package (see help of the different functions).

Value

If the number of methods is one, the result is the structure provided by the algorithm used.

If the number of methods is more than one, a list of results provided by each algorithm is given. Names of the list correspond to methods.

Author(s)

Cedric Gouy-Pailler ([e-mail])

Examples

# generating diagonal matrices
D <- replicate(30, diag(rchisq(df=1,n=10)), simplify=FALSE)
# Mixing and demixing matrices
B <- matrix(rnorm(100),10,10)
A <- solve(B)
C <- array(NA,dim=c(10,10,30))
for (i in 1:30) C[,,i] <- A %*% D[[i]] %*% t(A)
ajd(C,method=c("jedi","ffdiag"))