# Computes the constant edge weight that yields fastest averaging.

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    function [w,rho] = best_const(A)
    % [W,RHO] = BEST_CONST(A) gives a vector of the best constant edge weights
    % for a graph described by the incidence matrix A (NxM). N is the number of
    % nodes, and M is the number of edges. Each column of A has exactly one +1
    % and one -1.
    %
    % The best constant edge weight is the inverse of the average of
    % the second smallest and largest eigenvalues of the unweighted Laplacian:
    %    W = 2/( lambda_2(A*A') + lambda_n(A*A') )
    % RHO is computed from the weights W as follows:
    %    RHO = max(abs(eig( eye(n,n) - (1/n)*ones(n,n) - A*W*A' ))).
    %
    % For more details, see the references:
    % "Fast linear iterations for distributed averaging" by L. Xiao and S. Boyd
    % "Fastest mixing Markov chain on a graph" by S. Boyd, P. Diaconis, and L. Xiao
    % "Convex Optimization of Graph Laplacian Eigenvalues" by S. Boyd
    %
    % Almir Mutapcic 08/29/06
    [n,m] = size(A);

    % max degrees of the nodes
    Lunw = A*A';                % unweighted Laplacian matrix
    eigvals = sort(eig(Lunw));

    % max degree weigth allocation
    alpha = 2/(eigvals(2) + eigvals(n));
    w = alpha*ones(m,1);

    % compute the norm
    if nargout > 1,
        rho = norm( eye(n) - A*diag(w)*A' - (1/n)*ones(n) );
    end