30 Apr 2006
22 Oct 2008)
MatlabBGL provides robust and efficient graph algorithms for Matlab using native data structures.
function C = approx_multiway_cut(A,vs)
% APPROX_MULTIWAY_CUT Solve a 2-approximation to the multi-way cut problem
% C = approx_multiway_cut(A,vs)
% Outputs C, the set of edges cut in a 2-approximation to the multiway cut
% problem. The multiway-cut problem is to find a minimum cost set of edges
% to disconnect all the vertices in vs from each other.
% The non-zero values contain the weight of each edge.
% The input A must be a symmetric graph.
'the matrix must be symmetric.');
if (min(min(A)) < 0)
'the matrix cannot contain negative weights.');
n = size(A,1);
% this should be larger than any conceivable flow...
int_infinity = sum(sum(A))+2*sum(sum(A(vs,:)))+1;
% initial the cut to nothing.
C = sparse(n,n);
% Get A as an edge list...
[i j] = find(A);
v = vs(kk);
others = setdiff(vs,v);
% Each flow problem add a fake sink as the n+1 vertex
Aflow = A;
Aflow(others,n+1) = int_infinity*ones(length(others),1);
Aflow(n+1,:) = sparse(n+1,1);
% solve the max-flow problem
[flow ci] = max_flow(Aflow,v,n+1);
% remove the last (fake) entry from the cut.
ci = ci(1:end-1);
% construct a value over the edges that is 0 except on the cut, we know
% all values are positive, so just take the absolute value
vc = abs(v.*(ci(i)-ci(j)))./2;
% add the set of edges to the cut by constructing a sparse matrix with
% only the cut edges.
C = C+ sparse(i, j, vc, n,n);