// 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;
}
// 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 representativeA in _cuts[leftChild])
{
// All representatives of the cut G[rightChild, V \ rightChild]
foreach (BitSet representativeB 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(representativeB + _representative, leftChild, _graph);
BitSet representativeA_ = _cuts[vg - leftChild].GetRepresentative(dna);
// Find the representative Rb_ of the class [Ra ∪ Rw_]≡Vb_
DNeighborhood dnb = new DNeighborhood(representativeA + _representative, rightChild, _graph);
BitSet representativeB_ = _cuts[vg - rightChild].GetRepresentative(dnb);
// Find the representative Rw of the class [Ra ∪ Rb]≡Vw
DNeighborhood dnw = new DNeighborhood(representativeA + representativeB, 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][representativeA, representativeA_];
int rightValue = _tables[rightChild][representativeB, representativeB_];
// 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);
}
}
}
}
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;
}