# FGalgorithm-package.Rd: Execute the Flury and Gautschi diagonalisation algorithm,... In FGalgorithm: Flury and Gautschi algorithms

## Description

The minimization of the objective function

Φ (B) = {∏\limits_{i = 1}^k {≤ft[ {\frac{{\det (diag(B'{A_i}B))}}{{\det (B'{A_i}B)}}} \right]} ^{{n_i}}}

is required for a potpourri of statistical problems. This algorithm (Flury & Gautschi, 1984) is designed to find an orthogonal matrix B_0 of dimension p \times p such that

Φ (B) ≥ Φ (B_0)

for all orthogonal matrices B. The matrices A_1,...,A_k are positive-definite and are usually sample covariance matrices and n_is are positive real numbers.

It can be shown (Flury, 1983) that if B_0=[b_1, b_2,…, b_p ], then the following system of equations holds:

{b_l}'≤ft[{∑\limits_{i = 1}^k {{n_i}\frac{{{λ _{il}} - {λ _{ij}}}}{{{λ _{il}}{λ _{ij}}}}{A_i}} } \right]{b_j} = 0 \hspace{1cm} (l,j = 1, … ,p;l \not = j)

where

{λ _{ih}} = {b_h}^\prime {A_i}{b_h} \hspace{1cm} (i = 1, … ,k;h = 1, … ,p).

In other words, Flury and Gautschi algorithms find the solution B_0 of the above system of equations. Also, this algorithm can be used to find the maximum likelihood estimates of common principal components in k groups (Flury,1984).

## Details

 Package: FGalgorithm Type: Package Version: 1.0 Date: 2012-11-14 License: GPL (>= 2)