You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: joint_diag_r.m
    path of file: ./repos/OSET/external/jade
    
    the code of the file until where you have to start completion: function [ V ,  qDs ]= rjd(A,threshold)
%***************************************
% joint diagonalization (possibly
% approximate) of REAL matrices.
%***************************************
% This function minimizes a joint diagonality criterion
% through n matrices of size m by m.
%
% Input :

    
function [ V ,  qDs ]= rjd(A,threshold)
%***************************************
% joint diagonalization (possibly
% approximate) of REAL matrices.
%***************************************
% This function minimizes a joint diagonality criterion
% through n matrices of size m by m.
%
% Input :
% * the  n by nm matrix A is the concatenation of m matrices
%   with size n by n. We denote A = [ A1 A2 .... An ]
% * threshold is an optional small number (typically = 1.0e-8 see below).
%
% Output :
% * V is a n by n orthogonal matrix.
% * qDs is the concatenation of (quasi)diagonal n by n matrices:
%   qDs = [ D1 D2 ... Dn ] where A1 = V*D1*V' ,..., An =V*Dn*V'.
%
% The algorithm finds an orthogonal matrix V
% such that the matrices D1,...,Dn  are as diagonal as possible,
% providing a kind of `average eigen-structure' shared
% by the matrices A1 ,..., An.
% If the matrices A1,...,An do have an exact common eigen-structure
% ie a common othonormal set eigenvectors, then the algorithm finds it.
% The eigenvectors THEN are the column vectors of V
% and D1, ...,Dn are diagonal matrices.
%
% The algorithm implements a properly extend