Nested dissection permutation
Reorder Sparse Matrix Columns
Reorder a sparse matrix with several methods and compare the fill-in incurred by the LU decomposition of the reordered matrices.
west0479 matrix, which is a real-valued 479-by-479 sparse matrix with both real and complex pairs of conjugate eigenvalues. View the sparsity structure.
load west0479.mat A = west0479; spy(A)
Calculate several different permutations of the matrix columns, including the nested dissection ordering.
p1 = dissect(A); p2 = amd(A); p3 = symrcm(A);
Compare the sparsity structures of the LU decomposition of
A using the different ordering methods. The
dissect function produces the reordering that incurs the least amount of fill-in.
subplot(1,2,1) spy(A) title('Original Matrix') subplot(1,2,2) spy(lu(A)) title('LU Decomposition')
figure subplot(1,2,1) spy(A(p3,p3)) title('Reverse Cuthill-McKee') subplot(1,2,2) spy(lu(A(p3,p3))) title('LU Decomposition')
figure subplot(1,2,1) spy(A(p2,p2)) title('Approximate Minimum Degree') subplot(1,2,2) spy(lu(A(p2,p2))) title('LU Decomposition')
figure subplot(1,2,1) spy(A(p1,p1)) title('Nested Dissection') subplot(1,2,2) spy(lu(A(p1,p1))) title('LU Decomposition')
Filter Out Dense Columns
An arrowhead matrix is a sparse matrix that has a few dense columns. Use the
'MaxDegreeThreshold' name-value pair to filter the dense columns to the end of the reordered matrix.
Create an arrowhead sparse matrix and view the sparsity pattern.
A = speye(100) + diag(ones(1,99),1) + diag(ones(1,98),2); A(1:5,:) = ones(5,100); A = A + A'; spy(A)
Calculate the nested dissection ordering, and filter out the columns that have more than 10 nonzero elements.
p = dissect(A,'MaxDegreeThreshold',10);
View the sparsity pattern of the reordered matrix.
dissect places the dense columns at the end of the reordered matrix.
A — Input matrix
Input matrix, specified as a square matrix.
A can be
either full or sparse. If
A is nonsymmetric, then
dissect symmetrizes it.
Complex Number Support: Yes
Specify optional pairs of arguments as
the argument name and
Value is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name in quotes.
p = dissect(A,'NumIterations',15,'NumSeparators',2)
uses 15 refinement iterations and 2 separators in the nested dissection
VertexWeights — Vertex weights
Vertex weights, specified as the comma-separated pair consisting of
'VertexWeights' and a vector. The vector of
weights must have length equal to
size(A,1) so that a
weight is specified for each vertex. Use this option to specify how the
graph vertices (matrix columns) are weighted, which affects how the
algorithm computes the balance between partitions.
By default, the nested dissection algorithm weights all vertices equally.
NumSeparators — Number of separators
1 (default) | positive integer
Number of separators, specified as the comma-separated pair consisting
'NumSeparators' and a positive integer. Use this
option to specify how many partitions the graph is split into during
each partitioning step. Increasing the number of separators in the
nested dissection algorithm can result in a higher quality permutation
at the cost of additional execution time.
NumIterations — Number of refinement iterations
10 (default) | positive integer
Number of refinement iterations, specified as the comma-separated pair
'NumIterations' and a positive integer.
More refinement iterations can result in a higher quality permutation at
the cost of increased execution time.
MaxImbalance — Threshold for partition imbalance
1.2 (default) | scalar
Threshold for partition imbalance, specified as the comma-separated
pair consisting of
'MaxImbalance' and a scalar value
that is an integer multiple of
0.001 greater than or
1.001 and less than or equal to
1.999. Larger threshold values might reduce
execution time by allowing the algorithm to accept a worse
MaxDegreeThreshold — Threshold for vertex degree
0 (default) | nonnegative integer
Threshold for vertex degree, specified as the comma-separated pair
'MaxDegreeThreshold' and a nonnegative
integer. The nested dissection algorithm ignores vertices with degree
threshold*(avg degree)/10 during
dissect places vertices ignored in this
way at the end of the permutation. This effectively places any vertices
with degree greater than the threshold in the first, top-level
separator. Filtering out highly connected vertices can sometimes improve
the speed and accuracy of the ordering.
The default value of
0 means that all vertices are
p — Permutation vector
Permutation vector, returned as a vector. Use the permutation vector to
reorder the columns of
A using the indexing expression
A(p,p). For example, the Cholesky factorization
chol(A(p,p)) tends to be sparser than that of
The nested dissection ordering algorithm described in  is a multilevel graph partitioning algorithm that is used to produce fill-reducing orderings of sparse matrices. The input matrix is treated as the adjacency matrix of a graph. The algorithm coarsens the graph by collapsing vertices and edges, reorders the smaller graph, and then uses refinement steps to uncoarsen the small graph and produce a reordering of the original graph.
The name-value pairs for
dissect enable you to control various
stages of the algorithm:
In this phase, the algorithm creates successively smaller graphs from the original graph by collapsing together adjacent pairs of vertices.
'MaxDegreeThreshold'enables you to filter out highly connected graph vertices (which are dense columns in the matrix) by ordering them last.
After the graph is coarsened, the algorithm completely reorders the smaller graph. At each partitioning step, the algorithm attempts to partition the graph into equal parts:
'NumSeparators'specifies how many parts to partition the graph into,
'VertexWeights'optionally assigns weights to the vertices, and
'MaxImbalance'specifies the threshold for the difference in weight between the different partitions.
After the smallest graph is reordered, the algorithm makes projections to enlarge the graph back to the original size by expanding the vertices that were previously combined. After each projection step, a refinement step is performed that moves vertices between partitions to improve the quality of the solution.
'NumIterations'controls how many refinement steps are used during this uncoarsening phase.
 Karypis, George and Vipin Kumar. "A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs." SIAM Journal on Scientific Computing. Vol. 20, Number 1, 1999, pp. 359–392.
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Introduced in R2017b