minimum_spanning_tree <- function(C1, C2) stop("needs translation")
# function A = minimum_spanning_tree(C1, C2)
# %
# % Find the minimum spanning tree using Prim's algorithm.
# % C1(i,j) is the primary cost of connecting i to j.
# % C2(i,j) is the (optional) secondary cost of connecting i to j, used to break ties.
# % We assume that absent edges have 0 cost.
# % To find the maximum spanning tree, used -1*C.
# % See Aho, Hopcroft & Ullman 1983, "Data structures and algorithms", p 237.

# % Prim's is O(V^2). Kruskal's algorithm is O(E log E) and hence is more efficient
# % for sparse graphs, but is implemented in terms of a priority queue.

# % We partition the nodes into those in U and those not in U.
# % closest(i) is the vertex in U that is closest to i in V-U.
# % lowcost(i) is the cost of the edge (i, closest(i)), or infinity is i has been used.
# % In Aho, they say C(i,j) should be "some appropriate large value" if the edge is missing.
# % We set it to infinity.
# % However, since lowcost is initialized from C, we must distinguish absent edges from used nodes.

# n = length(C1);
# if nargin==1, C2 = zeros(n); end
# A = zeros(n);

# closest = ones(1,n);
# used = zeros(1,n); % contains the members of U
# used(1) = 1; % start with node 1
# C1(find(C1==0))=inf;
# C2(find(C2==0))=inf;
# lowcost1 = C1(1,:);
# lowcost2 = C2(1,:);

# for i=2:n
#   ks = find(lowcost1==min(lowcost1));
#   k = ks(argmin(lowcost2(ks)));
#   A(k, closest(k)) = 1;
#   A(closest(k), k) = 1;
#   lowcost1(k) = inf;
#   lowcost2(k) = inf;
#   used(k) = 1;
#   NU = find(used==0);
#   for ji=1:length(NU)
#     for j=NU(ji)
#       if C1(k,j) < lowcost1(j)
# 	lowcost1(j) = C1(k,j);
# 	lowcost2(j) = C2(k,j);
# 	closest(j) = k;
#       end
#     end
#   end
# end