function [ w, cvx_optval ] = fdla( A ) % [W,S] = FDLA(A) gives a vector of the fastest distributed linear averaging % edge weights for a graph described by the incidence matrix A (n x m). % Here n is the number of nodes and m is the number of edges in the graph; % each column of A has exactly one +1 and one -1. % % The FDLA edge weights are given by the SDP: % % minimize s % subject to -s*I <= I - L - (1/n)11' <= s*I % % where the variables are edge weights w in R^m and s in R. % Here L is the weighted Laplacian defined by L = A*diag(w)*A'. % The optimal value is s, and is returned in the second output. % % For more details see the references: % "Fast linear iterations for distributed averaging" by L. Xiao and S. Boyd % "Convex Optimization of Graph Laplacian Eigenvalues" by S. Boyd % % Written for CVX by Almir Mutapcic 08/29/06 [n,m] = size(A); I = eye(n,n); J = I - (1/n) * ones(n,n); cvx_begin sdp variable w(m,1) % edge weights variable s % epigraph variable variable L(n,n) symmetric minimize( s ) subject to L == A * diag(w) * A'; -s * I <= J - L <= s * I; cvx_end