Library Reference

LowerBoundAlgorithm

An algorithm for computing a lower bound to the treewidth of a graph. The options are

MMW <: LowerBoundAlgorithm

MMW()

The minor-min-width heuristic.

References

• Gogate, Vibhav, and Rina Dechter. "A complete anytime algorithm for treewidth." Proceedings of the 20th conference on Uncertainty in artificial intelligence. 2004.
• Bodlaender, Hans, Thomas Wolle, and Arie Koster. "Contraction and treewidth lower bounds." Journal of Graph Algorithms and Applications 10.1 (2006): 5-49.

lowerbound([weights, ]graph;
    alg::WidthOrAlgorithm=DEFAULT_LOWER_BOUND_ALGORITHM)

Compute a lower bound to the treewidth of a graph.

DEFAULT_LOWER_BOUND_ALGORITHM = MMW()

EliminationAlgorithm

A graph elimination algorithm computes a permutation of the vertices of a graph, which induces a chordal completion of the graph. The algorithms below generally seek to minimize the fill (number of edges) or width (largest clique) of the completed graph.

The following additional algorithms are implemented as package extensions and require loading an additional package.

Triangulation Recognition Heuristics

• MCS
• LexBFS
• MCSM
• LexM

These algorithms are guaranteed to compute perfect elimination orderings for chordal graphs. MCSM and LexM are variants of MCS and LexBFS that compute minimal orderings. The Lex algorithms were pubished first, and the MCS algorithms were introducd later as simplications. In practice, these algorithms work poorly on non-chordal graphs.

Bandwidth and Envelope Reduction Heuristics

• RCMMD
• RCMGL
• Spectral

These algorithms seek to minimize the bandwidth and profile of the permuted graph, quantities that upper bound the width and fill of theinduced chordal completion. RCMMD and RCMGL are two variants of the reverse Cuthill-McKee algorithm, a type of breadth-first search. They differ in in their choice of starting vertex. In practice, these algorithms work better than the triangulation recognition heuristics and worse than the greedy heuristics.

Greedy Heuristics

• MMD
• MF
• AMD
• SymAMD
• AMF

These algorithms simulate the elimination process, greedity selecting vertices to eliminate. MMD selects a vertex of minimum degree, and MF selects a vertex that induces the least fill. Updating the degree or fill of every vertex after elimination is costly; the algorithms AMD, SymAMD, and AMF are relaxations that work by approximating these values. The AMD algorithm is the state-of-the-practice for sparse matrix ordering.

Exact Treewidth Algorithms

• BT
• SAT

These algorithm minimizes the treewidth of the completed graph.

Meta Algorithms

• MinimalChordal
• CompositeRotations
• SafeRules
• ConnectedComponents

These algorithms are parametrized by another algorithm and work by transforming its input or output.

PermutationOrAlgorithm = Union{AbstractVector, EliminationAlgorithm}

Either a permutation or an algorithm.

DEFAULT_ELIMINATION_ALGORITHM = MMD()

The default algorithm.

BFS <: EliminationAlgorithm

BFS()

The breadth-first search algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = BFS()
BFS

julia> treewidth(graph; alg)
2

MCS <: EliminationAlgorithm

MCS()

The maximum cardinality search algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = MCS()
MCS

julia> treewidth(graph; alg)
3

References

• Tarjan, Robert E., and Mihalis Yannakakis. "Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs." SIAM Journal on Computing 13.3 (1984): 566-579.

LexBFS <: EliminationAlgorithm

LexBFS()

The lexicographic breadth-first-search algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = LexBFS()
LexBFS

julia> treewidth(graph; alg)
2

References

• Rose, Donald J., R. Endre Tarjan, and George S. Lueker. "Algorithmic aspects of vertex elimination on graphs." SIAM Journal on Computing 5.2 (1976): 266-283.

RCMMD{A} <: EliminationAlgorithm

RCMMD(alg::Algorithm)

RCMMD()

The reverse Cuthill-McKee algorithm. An initial vertex is selected using the minimum degree heuristic.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = RCMMD(QuickSort)
RCMMD:
    Base.Sort.QuickSortAlg()

julia> treewidth(graph; alg)
3

Parameters

• alg: sorting algorithm

References

• Cuthill, Elizabeth, and James McKee. "Reducing the bandwidth of sparse symmetric matrices." Proceedings of the 1969 24th National Conference. 1969.

RCMGL{A} <: EliminationAlgorithm

RCMGL(alg::Algorithm)

RCMGL()

The reverse Cuthill-McKee algorithm. An initial vertex is selected using George and Liu's variant of the GPS algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = RCMGL(QuickSort)
RCMGL:
    Base.Sort.QuickSortAlg()

julia> treewidth(graph; alg)
3

Parameters

• alg: sorting algorithm

References

• Cuthill, Elizabeth, and James McKee. "Reducing the bandwidth of sparse symmetric matrices." Proceedings of the 1969 24th National Conference. 1969.
• George, Alan, and Joseph WH Liu. "An implementation of a pseudoperipheral node finder." ACM Transactions on Mathematical Software (TOMS) 5.3 (1979): 284-295.

RCM = RCMGL

The default variant of the reverse Cuthill-Mckee algorithm.

LexM <: EliminationAlgorithm

LexM()

A minimal variant of the lexicographic breadth-first-search algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = LexM()
LexM

julia> treewidth(graph; alg)
2

References

• Rose, Donald J., R. Endre Tarjan, and George S. Lueker. "Algorithmic aspects of vertex elimination on graphs." SIAM Journal on Computing 5.2 (1976): 266-283.

MCSM <: EliminationAlgorithm

MCSM()

A minimal variant of the maximal cardinality search algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = MCSM()
MCSM

julia> treewidth(graph; alg)
2

References

• Berry, Anne, et al. "Maximum cardinality search for computing minimal triangulations of graphs." Algorithmica 39 (2004): 287-298.

AMD <: EliminationAlgorithm

AMD(; dense=10.0, aggressive=1.0)

The approximate minimum degree algorithm.

julia> using CliqueTrees

julia> import AMD as AMDLib

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = AMD(; dense=5.0, aggressive=2.0)
AMD:
    dense: 5.0
    aggressive: 2.0

julia> treewidth(graph; alg)
2

Parameters

• dense: dense row parameter
• aggressive: aggressive absorption

References

• Amestoy, Patrick R., Timothy A. Davis, and Iain S. Duff. "An approximate minimum degree ordering algorithm." SIAM Journal on Matrix Analysis and Applications 17.4 (1996): 886-905.

SymAMD <: EliminationAlgorithm

SymAMD(; dense_row=10.0, dense_col=10.0, aggressive=1.0)

The column approximate minimum degree algorithm.

julia> using CliqueTrees

julia> import AMD as AMDLib

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = SymAMD(; dense_row=5.0, dense_col=5.0, aggressive=2.0)
SymAMD:
    dense_row: 5.0
    dense_col: 5.0
    aggressive: 2.0


julia> treewidth(graph, alg)
2

Parameters

• dense_row: dense row parameter
• dense_column: dense column parameter
• aggressive: aggressive absorption

References

• Davis, Timothy A., et al. "A column approximate minimum degree ordering algorithm." ACM Transactions on Mathematical Software (TOMS) 30.3 (2004): 353-376.

AMF <: EliminationAlgorithm

AMF(; speed=1)

The approximate minimum fill algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = AMF(; speed=2)
AMF:
    speed: 2

julia> treewidth(graph; alg)
2

Parameters

• speed: fill approximation strategy (1, 2, or, 3)

References

• Rothberg, Edward, and Stanley C. Eisenstat. "Node selection strategies for bottom-up sparse matrix ordering." SIAM Journal on Matrix Analysis and Applications 19.3 (1998): 682-695.

MF <: EliminationAlgorithm

MF()

The greedy minimum fill algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = MF
MF

julia> treewidth(graph; alg)
2

References

• Tinney, William F., and John W. Walker. "Direct solutions of sparse network equations by optimally ordered triangular factorization." Proceedings of the IEEE 55.11 (1967): 1801-1809.

MMD <: EliminationAlgorithm

MMD(; delta=0)

The multiple minimum degree algorithm.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = MMD(; delta=1)
MMD:
    delta: 1

julia> treewidth(graph; alg)
2

Parameters

• delta: tolerance for multiple elimination

References

• Liu, Joseph WH. "Modification of the minimum-degree algorithm by multiple elimination." ACM Transactions on Mathematical Software (TOMS) 11.2 (1985): 141-153.

METIS <: EliminationAlgorithm

METIS(; ctype=-1, rtype=-1, nseps=-1, niter=-1, seed=-1,
        compress=-1, ccorder=-1, pfactor=-1, ufactor=-1)

The multilevel nested dissection algorithm implemented in METIS.

julia> using CliqueTrees, Metis

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = METIS(ctype=Metis.METIS_CTYPE_RM)
METIS:
    ctype: 0
    rtype: -1
    nseps: -1
    niter: -1
    seed: -1
    compress: -1
    ccorder: -1
    pfactor: -1
    ufactor: -1


julia> treewidth(graph; alg)
3

Parameters

• ctype: matching scheme to be used during coarsening
• rtype: algorithm used for refinement
• nseps: number of different separators computed at each level of nested dissection
• niter: number of iterations for refinement algorithm at each stage of the uncoarsening process
• seed: random seed
• compress: whether to combine vertices with identical adjacency lists
• ccorder: whether to order connected components separately
• pfactor: minimum degree of vertices that will be ordered last
• ufactor: maximum allowed load imbalance partitions

References

• Karypis, George, and Vipin Kumar. "A fast and high quality multilevel scheme for partitioning irregular graphs." SIAM Journal on Scientific Computing 20.1 (1998): 359-392.

Spectral <: EliminationAlgorithm

Spectral(; tol=0.0)

The spectral ordering algorithm only works on connected graphs. In order to use it, import the package Laplacians.

julia> using CliqueTrees, Laplacians

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = Spectral(; tol=0.001)
Spectral:
    tol: 0.001

julia> treewidth(graph; alg)
4

Parameters

• tol: tolerance for convergence

References

• Barnard, Stephen T., Alex Pothen, and Horst D. Simon. "A spectral algorithm for envelope reduction of sparse matrices." Proceedings of the 1993 ACM/IEEE Conference on Supercomputing. 1993.

FlowCutter <: EliminationAlgorithm

FlowCutter(; time=5, seed=0)

The FlowCutter algorithm.

julia> using CliqueTrees, FlowCutterPACE17_jll

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = FlowCutter(; time=2, seed=1)
FlowCutter:
    time: 2
    seed: 1

julia> treewidth(graph; alg)
2

Parameters

• time: run time
• seed: random seed

References

• Strasser, Ben. "Computing tree decompositions with flowcutter: PACE 2017 submission." arXiv preprint arXiv:1709.08949 (2017).

BT <: EliminationAlgorithm

BT()

The Bouchitte-Todinca algorithm.

julia> using CliqueTrees, TreeWidthSolver

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = BT()
BT

julia> treewidth(graph; alg)
2

References

• Korhonen, Tuukka, Jeremias Berg, and Matti Järvisalo. "Solving Graph Problems via Potential Maximal Cliques: An Experimental Evaluation of the Bouchitté-Todinca Algorithm." Journal of Experimental Algorithmics (JEA) 24 (2019): 1-19.

SAT{H, L, U} <: EliminationAlgorithm

SAT{H}(lb::WidthOrAlgorithn, ub::PermutationOrAlgorithm)

SAT{H}()

Compute a minimum-treewidth permutation using a SAT solver.

julia> using CliqueTrees, libpicosat_jll, PicoSAT_jll, CryptoMiniSat_jll, Lingeling_jll

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = SAT{libpicosat_jll}(MMW(), MF()) # picosat
SAT{libpicosat_jll, MMW, MF}:
    MMW
    MF

julia> alg = SAT{PicoSAT_jll}(MMW(), MF()) # picosat
SAT{PicoSAT_jll, MMW, MF}:
    MMW
    MF

julia> alg = SAT{CryptoMiniSat_jll}(MMW(), MF()) # cryptominisat
SAT{CryptoMiniSat_jll, MMW, MF}:
    MMW
    MF

julia> alg = SAT{Lingeling_jll}(MMW(), MF()) # lingeling
SAT{Lingeling_jll, MMW, MF}:
    MMW
    MF

julia> treewidth(graph; alg)
2

Parameters

• lb: lower bound algorithm
• ub: upper bound algorithm

References

• Samer, Marko, and Helmut Veith. "Encoding treewidth into SAT." Theory and Applications of Satisfiability Testing-SAT 2009: 12th International Conference, SAT 2009, Swansea, UK, June 30-July 3, 2009. Proceedings 12. Springer Berlin Heidelberg, 2009.
• Berg, Jeremias, and Matti Järvisalo. "SAT-based approaches to treewidth computation: An evaluation." 2014 IEEE 26th international conference on tools with artificial intelligence. IEEE, 2014.
• Bannach, Max, Sebastian Berndt, and Thorsten Ehlers. "Jdrasil: A modular library for computing tree decompositions." 16th International Symposium on Experimental Algorithms (SEA 2017). Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2017.

MinimalChordal{A} <: EliminationAlgorithm

MinimalChordal(alg::PermutationOrAlgorithm)

MinimalChordal()

Evaluate an elimination algorithm, and them improve its output using the MinimalChordal algorithm. The result is guaranteed to be minimal.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg1 = MCS()
MCS

julia> alg2 = MinimalChordal(MCS())
MinimalChordal{MCS}:
    MCS

julia> label1, tree1 = cliquetree(graph; alg=alg1);

julia> label2, tree2 = cliquetree(graph; alg=alg2);

julia> FilledGraph(tree1) # more edges
{8, 12} FilledGraph{Int64, Int64}

julia> FilledGraph(tree2) # fewer edges
{8, 11} FilledGraph{Int64, Int64}

Parameters

• alg: elimination algorithm

References

• Blair, Jean RS, Pinar Heggernes, and Jan Arne Telle. "A practical algorithm for making filled graphs minimal." Theoretical Computer Science 250.1-2 (2001): 125-141.

CompositeRotations{C, A} <: EliminationAlgorithm

CompositeRotations(clique::AbstractVector, alg::EliminationAlgorithm)

CompositeRotations(clique::AbstractVector)

Evaluate an eliminaton algorithm, ensuring that the given clique is at the end of the ordering.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg = CompositeRotations([2], MCS())
CompositeRotations{Vector{Int64}, MCS}:
    clique: [2]
    MCS

julia> order, index = permutation(graph; alg);

julia> order # 2 is the last vertex in the ordering
8-element Vector{Int64}:
 4
 5
 7
 8
 3
 6
 1
 2

Parameters

• clique: clique
• alg: elimination algorithm

References

• Liu, Joseph WH. "Equivalent sparse matrix reordering by elimination tree rotations." Siam Journal on Scientific and Statistical Computing 9.3 (1988): 424-444.

SafeRules{L, U} <: EliminationAlgorithm

SafeRules(lb::WidthOrAlgorithm, ub::EliminationAlgororithm)

SafeRules()

Preprocess a graph using safe reduction rules. The algorithm lb is used to compute a lower bound to the treewidth; better lower bounds allow the algorithm to perform more reductions.

julia> using CliqueTrees, TreeWidthSolver

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> alg1 = BT()
BT

julia> alg2 = SafeRules(MMW(), BT())
SafeRules{MMW, BT}:
    MMW
    BT

julia> @time treewidth(graph; alg=alg1) # slower
  0.000177 seconds (1.41 k allocations: 90.031 KiB)
2

julia> @time treewidth(graph; alg=alg2) # faster
  0.000066 seconds (237 allocations: 16.875 KiB)
2

Parameters

• lb: lower bound algorithm
• ub: elimination algorithm

References

• Bodlaender, Hans L., et al. "Pre-processing for triangulation of probabilistic networks." (2001).
• Bodlaender, Hans L., Arie M.C.A. Koster, and Frank van den Eijkhof. "Preprocessing rules for triangulation of probabilistic networks." Computational Intelligence 21.3 (2005): 286-305.
• van den Eijkhof, Frank, Hans L. Bodlaender, and Arie M.C.A. Koster. "Safe reduction rules for weighted treewidth." Algorithmica 47 (2007): 139-158.

SafeSeparators{M, A} <: EliminationAlgorithm

SafeSeparators(min::PermutationOrAlgorithm, alg::EliminationAlgorithm)

SeparatorReducton()

Apple an elimination algorithm to the atoms of an almost-clique separator decomposition. The algorithm min is used to compute the decomposition.

Parameters

• min: minimal elimination algorithm
• alg: elimination algorithm

References

• Bodlaender, Hans L., and Arie MCA Koster. "Safe separators for treewidth." Discrete Mathematics 306.3 (2006): 337-350.
• Tamaki, Hisao. "A heuristic for listing almost-clique minimal separators of a graph." arXiv preprint arXiv:2108.07551 (2021).

ConnectedComponents{A} <: EliminationAlgorithm

ConnectedComponents(alg::PermutationOrAlgorithm)

ConnectedComponents()

Apply an elimination algorithm to each connected component of a graph.

Parameters

• alg: elimination algorithm

permutation([weights, ]graph;
    alg::PermutationOrAlgorithm=DEFAULT_ELIMINATION_ALGORITHM)

Construct a fill-reducing permutation of the vertices of a simple graph.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> order, index = permutation(graph);

julia> order
8-element Vector{Int64}:
 4
 1
 2
 8
 5
 3
 6
 7

julia> index == invperm(order)
true

mcs(graph[, clique])

Perform a maximum cardinality search, optionally specifying a clique to be ordered last. Returns the inverse permutation.

SupernodeType

A type of supernode partition. The options are

DEFAULT_SUPERNODE_TYPE = Maximal()

The default supernode partition.

Nodal <: SupernodeType

A nodal supernode partition.

Maximal <: SupernodeType

A maximal supernode partition.

Fundamental <: SupernodeType

A fundamental supernode partition.

SinglyLinkedList{I, Head, Next} <: AbstractLinkedList{I}

SinglyLinkedList{I}(n::Integer)

A singly linked list of distinct natural numbers. This type supports the iteration interface.

julia> using CliqueTrees

julia> list = SinglyLinkedList{Int}(10)
SinglyLinkedList{Int64, Array{Int64, 0}, Vector{Int64}}:

julia> pushfirst!(list, 4, 5, 6, 7, 8, 9)
SinglyLinkedList{Int64, Array{Int64, 0}, Vector{Int64}}:
 4
 5
 6
 7
 8
 ⋮

julia> collect(list)
6-element Vector{Int64}:
 4
 5
 6
 7
 8
 9

AbstractTree{V} = Union{Tree{V}, SupernodeTree{V}, CliqueTree{V}}

A rooted forest. This type implements the indexed tree interface.

rootindices(tree::AbstractTree)

Get the roots of a rooted forest.

firstchildindex(tree::AbstractTree, i::Integer)

Get the first child of node i. Returns nothing if i is a leaf.

ancestorindices(tree::AbstractTree, i::Integer)

Get the proper ancestors of node i.

Tree{V} <: AbstractUnitRange{V}

Tree(tree::AbstractTree)

Tree{V}(tree::AbstractTree) where V

A rooted forest with vertices of type V. This type implements the indexed tree interface.

eliminationtree([weights, ]graph;
    alg::PermutationOrAlgorithm=DEFAULT_ELIMINATION_ALGORITHM)

Construct a tree-depth decomposition of a simple graph.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> label, tree = eliminationtree(graph);

julia> tree
8-element Tree{Int64}:
 8
 └─ 7
    ├─ 5
    └─ 6
       ├─ 1
       ├─ 3
       │  └─ 2
       └─ 4

SupernodeTree{V} <: AbstractVector{UnitRange{V}}

A supernodal elimination tree with vertices of type V. This type implements the indexed tree interface.

supernodetree(graph;
    alg::PermutationOrAlgorithm=DEFAULT_ELIMINATION_ALGORITHM,
    snd::SupernodeType=DEFAULT_SUPERNODE_TYPE)

Construct a supernodal elimination tree.

Clique{V, E} <: AbstractVector{V}

A clique of a clique tree.

CliqueTree{V, E} <: AbstractVector{Clique{V, E}}

A clique tree with vertices of type V and edges of type E. This type implements the indexed tree interface.

cliquetree([weights, ]graph;
    alg::PermutationOrAlgorithm=DEFAULT_ELIMINATION_ALGORITHM,
    snd::SupernodeType=DEFAULT_SUPERNODE_TYPE)

Construct a tree decomposition of a simple graph. The vertices of the graph are first ordered by a fill-reducing permutation computed by the algorithm alg. The size of the resulting decomposition is determined by the supernode partition snd.

julia> using CliqueTrees

julia> graph = [
           0 1 0 0 0 0 0 0
           1 0 1 0 0 1 0 0
           0 1 0 1 0 1 1 1
           0 0 1 0 0 0 0 0
           0 0 0 0 0 1 1 0
           0 1 1 0 1 0 0 0
           0 0 1 0 1 0 0 1
           0 0 1 0 0 0 1 0
       ];

julia> label, tree = cliquetree(graph);

julia> tree
6-element CliqueTree{Int64, Int64}:
 [6, 7, 8]
 └─ [5, 7, 8]
    ├─ [1, 5]
    ├─ [3, 5, 7]
    │  └─ [2, 3]
    └─ [4, 5, 8]

treewidth([weights, ]tree::CliqueTree)

Compute the width of a clique tree.

treewidth([weights, ]graph;
    alg::PermutationOrAlgorithm=DEFAULT_ELIMINATION_ALGORITHM)

Compute an upper bound to the tree width of a simple graph.

separator(clique::Clique)

Get the separator of a clique.

separator(tree::CliqueTree, i::Integer)

Get the separator at node i.

residual(clique::Clique)

Get the residual of a clique.

residual(tree::CliqueTree, i::Integer)

Get the residual at node i.

FilledGraph{V, E} <: AbstractGraph{V}

A filled graph.

ischordal(graph)

Determine whether a simple graph is chordal.

isperfect(graph, order::AbstractVector[, index::AbstractVector])

Determine whether an fill-reducing permutation is perfect.