// Given a vertex w, we can update the dNeighborhood of a set X to reflect the dNeighborhood of the set X + w in O(n) time
public dNeighborhood CopyAndUpdate(Graph graph, int w)
{
// initialize an empty dNeighborhood in O(|Vector|) time
dNeighborhood nx = new dNeighborhood(Vector);
// Copy all values in O(|Vector|) time
foreach (int v in Vector)
nx.Occurrences[v] = Occurrences[v];
// Foreach vertex in N(w) * Vector they will appear one extra time in the multiset
// This operation take O(n) time because of the bitset operation
foreach (int v in graph.OpenNeighborhood(w) * Vector)
nx.Occurrences[v] = Math.Min(dValue, nx.Occurrences[v] + 1);
return nx;
}
public dNeighborhood CopyAndUpdateVector(BitSet vector, bool increment)
{
// initialize an empty dNeighborhood in O(|Vector|) time
dNeighborhood nx = new dNeighborhood(vector);
BitSet iterateOver = increment ? Vector : vector;
foreach (int v in iterateOver)
nx.Occurrences[v] = Occurrences[v];
return nx;
}
/*************************/
// Basic operations
/*************************/
// When adding a new entry we check if the neighborhood is already contained in the set
// If true, then we might replace the previous representative that is not connected to this neighborhood if the new one is lexicographically smaller
public void Update(BitSet representative, dNeighborhood neighborhood)
{
Map.Add(representative.Copy(), neighborhood);
}
// Returns the representative belonging to a given neighborhood
public BitSet GetRepresentative(dNeighborhood neighborhood)
{
return Map.Reverse[neighborhood];
}
public bool ContainsNeighborhood(dNeighborhood neighborhood)
{
return Map.ContainsValue(neighborhood);
}
// Recursively fills all tables for each node of the decomposition tree in a bottom up fashion
// Node W is the current node we are working on
private void FillTable(BitSet node)
{
// Initialize a new table of node W
Tables[node] = new Table();
// All vertices of the graph
BitSet VG = Graph.Vertices;
int n = Graph.Size;
// The base case for leaf nodes is handled seperately
if (node.Count == 1)
{
FillLeaf(node);
return;
}
// If the node has a size >1 then there it has child nodes
BitSet leftChild = Tree.LeftChild[node];
BitSet rightChild = Tree.RightChild[node];
// We work in a bottom-up fashion, so we first recurse on the two children of this node
// We know that this node is not a leaf since we already passed the check for leaf-nodes
// We also know that this node has two children, since having 1 child in a boolean decomposition means that the parent and child node are the same and thus redundant
FillTable(leftChild);
FillTable(rightChild);
// Initially set all combinations of representatives to the worst value, meaning that no solution exists
foreach (BitSet representative in Cuts[node])
foreach (BitSet _representative in Cuts[VG - node])
Tables[node][representative, _representative] = Optimum.PessimalValue;
// All representatives of the cut G[leftChild, V \ leftChild]
foreach (BitSet representative_a in Cuts[leftChild])
{
// All representatives of the cut G[rightChild, V \ rightChild]
foreach (BitSet representative_b in Cuts[rightChild])
{
// All representatives of the cut G[V \ node, node]
foreach (BitSet _representative in Cuts[VG - node])
{
// Find the representative Ra_ of the class [Rb ∪ Rw_]≡Va_
dNeighborhood dna = new dNeighborhood(representative_b + _representative, leftChild, Graph);
BitSet _representative_a = Cuts[VG - leftChild].GetRepresentative(dna);
// Find the representative Rb_ of the class [Ra ∪ Rw_]≡Vb_
dNeighborhood dnb = new dNeighborhood(representative_a + _representative, rightChild, Graph);
BitSet _representative_b = Cuts[VG - rightChild].GetRepresentative(dnb);
// Find the representative Rw of the class [Ra ∪ Rb]≡Vw
dNeighborhood dnw = new dNeighborhood(representative_a + representative_b, VG - node, Graph);
BitSet representative = Cuts[node].GetRepresentative(dnw);
// Optimal size of this sigma,rho set in the left and right child
int leftValue = Tables[leftChild][representative_a, _representative_a];
int rightValue = Tables[rightChild][representative_b, _representative_b];
// Some hoops to avoid integer overflowing
int combination = leftValue == Optimum.PessimalValue || rightValue == Optimum.PessimalValue ?
Optimum.PessimalValue : leftValue + rightValue;
// Fill the optimal value that we can find in the current entry
Tables[node][representative, _representative] = Optimum.Optimal(Tables[node][representative, _representative], combination);
}
}
}
}
// Implementation of Algorithm 1 of 'Fast dynamic programming for locally checkable vertex subset and vertex partitioning problems' (Bui-Xuan et al.)
// Used to compute the representatives and their corresponding dNeighborhoods for a given node of the decomposition tree
private void FillTable(Graph graph, BitSet cut)
{
int n = graph.Size;
BitSet _cut = graph.Vertices - cut;
// Lists of representatives that we keep track of on each level of the algorithm
RepresentativeList representatives = new RepresentativeList();
RepresentativeList lastLevel = new RepresentativeList();
// Initial insertions, the empty set always has the empty neighborhood set as a representative initially
dNeighborhood dInitial = new dNeighborhood(_cut);
representatives.Update(new BitSet(0, n), dInitial);
lastLevel.Update(new BitSet(0, n), dInitial);
while (lastLevel.Count > 0)
{
RepresentativeList nextLevel = new RepresentativeList();
foreach (BitSet r in lastLevel)
{
foreach (int v in cut)
{
// avoid that r_ = r, since we already saved all sets of that size
if (r.Contains(v))
continue;
BitSet r_ = r + v;
dNeighborhood dn = representatives.GetNeighborhood(r).CopyAndUpdate(graph, v);
if (!representatives.ContainsNeighborhood(dn) && !dn.Equals(representatives.GetNeighborhood(r)))
{
nextLevel.Update(r_, dn);
representatives.Update(r_, dn);
}
}
}
// Update the values for the next iteration
lastLevel = nextLevel;
}
// Save the representatives at the current cut in the table
Table[cut.Copy()] = representatives;
// Save the maximum size that we encounter during all iterations; this will be the boolean dimension of the graph is d = 1.
MaxDimension = Math.Max(MaxDimension, representatives.Count);
}
