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Title: A Column approximate minimum degree ordering algorithm
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Title: A Column approximate minimum degree ordering algorithm
Series Title: Department of Computer and Information Science and Engineering Technical Report ; 00-005
Physical Description: Book
Language: English
Creator: Davis, Timothy A.
Gilbert, John R.
Larimore, Stefan I.
Ng, Esmond G.
Affiliation: University of Florida -- Department of Computer and Informatin Science and Engineering
Xerox Palo Alto Research Center
Lawrence Berkeley National Laboratory
Publisher: Department of Computer and Information Science and Engineering, University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: October 16, 2000
Copyright Date: 2000
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Source Institution: University of Florida
Holding Location: University of Florida
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A column approximate minimum degree

ordering algorithm *

Timothy A. Davis t John R. Gilbert t
Stefan I. Larimore s Esmond G. Ng

October 16, 2000

Technical Report TR-00-005. Department of Computer and Information
Science and Engineering, University of Florida. October, 2000.

Sparse Gaussian elimination with partial pivoting computes the
factorization PAQ = LU of a sparse matrix A, where the row ordering
P is selected during factorization using standard partial pivoting with
row interchanges. The goal is to select a column pi .. .. i!:_. Q, based
solely on the nonzero pattern of A such that the factorization remains
as sparse as possible, regardless of the subsequent choice of P. The
choice of Q can have a dramatic impact on the number of nonzeros
in L and U. One scheme for determining a good column ordering
for A is to compute a symmetric ordering that reduces fill-in in the
C'!.I. -!:5 factorization of ATA. This approach, which requires the
sparsity structure of ATA to be computed, can be expensive both in
*This work was supported in part by the National Science Foundation under grant
number DMS-9803599; in part by the Director, Office of Science, Division of Mathematical,
Information, and Computational Sciences of the U.S. Department of Energy under contract
number DE-ACO3-7'.II .-"" r i, and in part by DARPA under contract DABT63-95-C-0087.
tComputer and Info. Sci. and Eng. Dept., University of Florida, Gainesville, FL, USA.
tXerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304-1314.
e-mail: gilbert
Microsoft, Inc. e-mail: slarimor
Lawrence Berkeley National Laboratory, One Cyclotron Road, Mail Stop 50F, Berke-
ley, CA -7_'II e-mail:

terms of space and time since ATA may be much denser than A. An
alternative is to compute Q directly from the sparsity structure of A;
this strategy is is used by Matlab's colmmd preordering algorithm. A
new ordering algorithm, colamd, is presented. It is based on the same
strategy but uses a better ordering heuristic. Colamd is faster and
computes better orderings, with fewer nonzeros in the factors of the

Categories and Subject Descriptors: G.1.3 [Numerical Analysis]: Numer-
ical Linear Algebra -linear s;,;-. i,- (direct methods), sparse and very 7r,.,
s;,; -. G.4 [\l ii i1 s' of Computing]: Mathematical Software -algo-
rithm O,.,Il-.. eff. ";. '
General terms: Algorithms, Experimentation, Performance
Keywords: sparse nonsymmetric matrices, linear equations, ordering meth-

1 Introduction

Sparse Gaussian elimination with partial pivoting computes the factoriza-
tion PAQ = LU for the sparse nonsymmetric matrix A, where P and Q
are permutation matrices, L is a lower triangular matrix, and U is an upper
triangular matrix. Gilbert and Peierls [30] have shown that sparse partial
pivoting can be implemented in time proportional to the number of floating-
point operations required. The method is used by Matlab when solving a
system of linear equations Ax = b when A is sparse [27]. An improved
implementation is in a sparse matrix package, SuperLU [11]. The solution
process starts by finding a sparsity-preserving permutation Q. Next, the per-
mutation P is selected during numerical factorization using standard partial
pivoting with row interchanges. The permutation P is selected without re-
gard to sparsity. Our goal is to compute a sparsity-preserving permutation
Q solely from the pattern of A such that the LU factorization PAQ = LU
remains as sparse as possible, regardless of the subsequent choice of P. Our
resulting code has been incorporated in SuperLU and in Matlab Version 6.
Section 2 provides the theoretical background for our algorithm. In Sec-
tion 3, we describe symbolic LU factorization (with no column interchanges)
as a precursor to the colamd ordering algorithm presented in Section 4. Sec-
tion 4 also describes the various metrics for selecting columns that we evalu-
ated in the design of the code. Experimental results for square nonsymmetric
matrices, rectangular matrices, and symmetric matrices are presented in Sec-
tion 5. Section 6 presents our conclusions, and describes how to obtain the
colamd and symamd codes.
Our notation is as follows. Set subtraction is denoted by the "\" oper-
ator. We use I...| to denote either the absolute value of a scalar, the num-
ber of nonzero entries in a matrix, or the size of a set. The usage will
be clear in context. The structure of a matrix A, denoted by Struct(A),
is a set that contains the locations of the nonzero entries in A; that is,
Struct(A) = {(i,j) : Aij / 0}. Throughout this paper, and in the algo-
rithms, we assume that exact numerical cancellations do not occur. Some
entries in the matrix may happen to become zero during factorization (be-
cause of accidental cancellation); we still refer to these as "ir i. i. entries.

2 ATA Orderings

Let A be a given nonsymmetric matrix and assume that it is nonsingular. It
follows from Duff [14] that the rows (or the columns) of A can be permuted
so that the diagonal entries of the permuted A are all nonzero. We therefore
assume throughout this paper that the given matrix A has been permuted
accordingly so that it has a zero-free diagonal.
Suppose that L and U are the triangular factors obtained when Gaussian
elimination with partial pivoting is applied to A. That is, PA = LU, for
some row permutation P. Consider the symmetric positive definite matrix
ATA, which has a Cholesky factorization ATA LcLl with Lc being lower
triangular. George and Ng [25] showed that if A has a zero-free diagonal,
then the pattern of Lc + LT includes the patterns of L and U, regardless of
the row permutation used in partial pivoting. We summarize the result in
the following theorem.

Theorem 2.1 (George and Ng [25]) Let A be a nonr-:,.llil and non-
,iiiniii, .lii matrix that has a zero-free diagonal. Let Lc denote the Cholesky
factor of ATA. Let L and U be the LU factors of A obtained by partial
pivoting. Then Struct(U) C Struct(L ), and the entries within each col-
umn of L can be rearriI.g Ito ;,. 1.1 a t, .:.i,,',lr matrix L with Struct(L) C

Gilbert and Ng [28] also showed that the bound on U is tight when A is
a strong Hall matrix.1

Theorem 2.2 (Gilbert and Ng [28]) Let A be a no,'...:,,il,, and non-
,,,,,-iiiiiil matrix that has a zero-free diagonal. Assume that A is strong
Hall. Let Lc denote the ChI,, l..i factor of ATA. Let L and U be the LU fac-
tors of A obtained by partial pivoting. For any choice of (i, j) Struct(LL),
there exists an assignment of numerical values to the nonzero entries of A
such that Uij / 0.

It is well known that the sparsity of Lc depends drastically on the way in
which the rows and columns of ATA are permuted [23]. Thus, Theorems 2.1
1A matrix A is strong Hall if every set of k columns of A, 1 < k < n 1, contain at
least k + 1 nonzero rows. A strong Hall matrix is irreducible. See Coleman et al. [6] for

and 2.2 ir-:.- -I a way to reduce the amount of fill in L and U [25, 26].
Given a matrix A, we first form the pattern of ATA. Then we compute a
symmetric ordering Q of ATA to reduce the amount of fill in Lc. Finally,
we apply the permutation Q to the columns of A.
The main disadvantage with the approach above is the cost of forming
ATA. Even if A is sparse, the product ATA may be dense. Consequently,
the time and storage required to form the product may be high. The primary
goal of this paper is to describe ordering algorithms that compute Q from
the pattern of A without explicitly forming ATA.

3 Symbolic LU factorization

Our column ordering technique is based on a symbolic analysis of the conven-
tional outer-product formulation of Gaussian elimination of the n-b,\--, ma-
trix A with row interchanges to maintain numerical stability. Let A() A.
For k 1, 2, ..., n 1, let A(k) denote the bottom right (n k)-by-(n k)
submatrix of A(k-1) after Gaussian elimination is performed. We assume
that the rows and columns of A(k) are labeled from k + 1 to n.
At the k-th step of Gaussian elimination, one finds the entry with the
largest magnitude in column k of A(k-l) and swaps rows to place this entry
on the diagonal. Column k of L is then a scaled version of column k of A(k-1),
and row k of U is row k of A(k-1). The outer product of the column k of L
and the row k of U is subtracted from A(k-1) to give A(k). The result is the
factorization PA = LU, where P is the permutation matrix determined by
the row interchanges. When A is sparse, the update using the outer product
may turn some of the zero entries into nonzero. These new nonzero entries
are referred to as fill-in. The amount of fill-in that occurs depends on the
order in which the columns of A are eliminated [23]. Our goal, therefore, is to
find a column ordering Q of the matrix A prior to numerical factorization, so
that the LU factorization of the column-permuted matrix AQ is sparser than
the LU factorization of the original matrix A, regardless of how P is chosen.
Another application of column orderings is sparse QR factorization [33].
In order to control the fill-in that occurs when A is sparse, we need to
know the nonzero patterns of the matrices A(k), for each k from 0 to n 1.
From this, we can determine a column ordering Q that attempts to keep the
factorization of AQ sparse as the factorization progresses.
We must compute symbolic patterns L, U, and A(k) that account for all

possible row interchanges that may occur during numerical factorization; the
actual structures of L, U, and A(k) during the factorization of PA with any
specific row permutation P will be subsets of these patterns.
Let k and Uk denote the nonzero patterns of column k of L and row k
of U, respectively. For i > k, let 7 k) denote the set of column indices of
nonzero entries in row i of A(k),

7> {j>k:a / O}.

Similarly, for j > k, let Ck) denote the set of row indices of nonzero entries
in column j of A(k),
C) = {i > k : ) / o}.
Note that these definitions imply

j R cZ i C(k) (1)

for all k, i, and j.
We now describe how to compute k, Uk, and Ak in order of increasing
k. We begin with A() Struct(A()), and with A() A.
Using the assumption that A has a zero-free diagonal, it is easy to see
that the pattern of the k-th column of L is simply column k of A(k-l), so we
may take
Lk Ck)
regardless of how the nonzero numerical values are interchanged. Consider
the possible nonzero pattern of the k-th row of U after partial pivoting. Any
row i can be selected as the pivot row for which a 1) is nonzero. This means
the potential candidate pivot rows correspond to the set Lk. The pattern of
the k-th pivot row is bounded by the union of all candidate pivot rows [26],
so we may take
Uk U 1)

If an arbitrary row r E k is selected as the pivot row, then R k- 1) C Uk
for all i E k, so that Uk can accommodate any row interchanges due to
numerical partial pivoting.
In the outer product step, multiples of the pivot row are added to each
row i in the pivot column. Since k-l) C Uk, the pattern of each row i in
k \ {k} of the matrix in A(k) after this outer product step must be contained

in the set Uk \ {k}. Since we do not know which row is the pivot row, we can
bound the pattern of row i of A(k) with

Rn k) \ {k}.

Note that all rows i in k \ {k} now have the same pattern, and we can
save time and space by not storing all of them. Any step that modifies one
of these rows will modify all of them, and the rows will again have the same
nonzero pattern. Let p min Uk \ {k}. At step p, Lk \ {k} c L~, and so
R p = U,\{p} for all i in k\{k}. Thus, at step k, we can replace all rows i in
k\{k} with a single super-row RT7k) Uk\{k} that represents the pattern all
of the rows i in the set k \ {k}. The integer r is an arbitrary place-holder; in
our symbolic factorization algorithm we choose an arbitrary row index from
the set Ck 1). We define [r] as the set of rows (with size I[r]1) represented by
the super-row r. When the symbolic factorization starts, every row belongs
to exactly one set; i.e., [r] {r}. To compute the symbolic update, we
need only place the single representative, r, into the set Ck), and we can
remove the discarded rows k \ {r}. Sherman [43] introduced the concept of
super-columns in the context of sparse C'!. .! -l:y factorization.
With these simplifications, at step k the sets R and C represent a bound
on the pattern of A(k) for LU factorization. They are also a quotient graph
representation [17, 21] of the matrix obtained at the k-th step of the C'! .1 -l:y
factorization of ATA. Our column preordering method does not require the
patterns of L and U, so these can be discarded. As a result, the algorithm
shown in Figure 1 requires only O(|A|) space. Superscripts on R and C are
dropped for clarity.
The initial storage required is O(IAI). At step k,

Ir1 < E 1Ril
because R7 is computed as

Rr -= U R \ {k}. (2)

The sets R7 for all i in Ck \ {r} are then discarded. When the outer product
is formed, each column Cj (where j cE 7) reduces in size or stays the same
size. If j E r,, then from (2) this implies 3i E Ck such that j E 7R. From

Symbolic LU factorization algorithm
Let = {j : aij / 0} for all i
Let Cj {i f: aij / 0} for all j
for k = 1 to n do
determine pattern for pivot row and column:
Let r be an arbitrary index in Ck
(Uiec, R) \ {k}
.,iil',..1'. outer product update:
for ie Ck \ {r} do

end for
for j E R, do
C, (C, \ Ck) U {r}
end for
Ck 0
end for

Figure 1: Symbolic LU factorization algorithm

(1), j E Ri implies that i E Cj. Thus, 3i E Ck Cj, which implies that
|C, \ Ck\ < Cjl. Thus, each step strictly reduces the total storage required
for the pattern of A(k).
The total time taken is dominated by the symbolic update. Computing
the pivot rows 7R for the entire algorithm takes a total of O(IU|) time, since
R, = Uk \ {k} is the pattern of the off-diagonal part of row k of U, and since
R, is discarded when it is included in a set union for a subsequent pivot
row. Modifying a single column Cj in the symbolic update takes no more
than O(IA,jl) time, since ICjl < IA,j. Column j is modified at most once
for each row Uk that contains column index j. Thus, column j is modified
at most Ij I times during the entire algorithm. Here U,j is the upper bound
pattern on column j of the matrix U for any row permutation P due to
partial pivoting. Thus, the total time taken is

o EI |Ajl|UI

where the nonzero pattern of U can accommodate any row permutation P
due to partial pivoting. This is the same time required to compute the sparse
matrix product AUT, and is typically much less than the time required for
numerical factorization, which is equal to the time required to compute the
sparse matrix product L times U [31],

0 ( L gk Uk *)

Faster methods exist to compute this upper bound on symbolic factoriza-
tion with partial pivoting. George and Ng's method [26] for computing the
patterns of L and U takes only O(L + IUI) time if the column ordering is
fixed. The method does not produce the nonzero patterns of A(k), however,
so it cannot be used as a basis for the colamd algorithm described in the
next section.

4 The colamd ordering algorithm

We now present a column ordering method that is based on the symbolic
LU factorization algorithm presented in the last section and that has the
same time complexity and storage requirements. At step k, we select a pivot

column c to minimize some metric on all of the candidate pivot columns as
an attempt to reduce fill-in. Columns c and k are then exchanged.
The presence of dense (or nearly dense) rows or columns in A can greatly
affect both the ordering quality and run time. A single dense row in A ren-
ders all our bounds useless; the bound on the nonzero pattern of A(1 is a
completely dense matrix. We can hope that this dense row will not be se-
lected as the first pivot row during numerical factorization, and withhold the
row from the ordering algorithm. Dense columns do not affect the ordering
quality, but '1!, i, do increase the ordering time. A single dense column in-
creases the ordering time by O(n2). Dense columns are withheld from the
symbolic factorization and ordering algorithm, and placed last in the column
ordering Q. Determining how dense a row or column should be for it to be
ignored is problem dependent. We used the same default threshold used by
Matlab, 50' which is probably too high for most matrices.
Taking advantage of super-columns can greatly reduce the ordering time.
In the symbolic update step, we look at all columns j in the set R,. If any two
or more columns have the same pattern (tested via a hash function [2]), 1, i,-
are merged into a single super-column. This saves both time and storage in
subsequent steps. Selecting a super-column c allows us to mass-eliminate [24]
all columns [c] represented by the super-column c, and we skip ahead to step
k + I[c]| of symbolic factorization. If the pattern of column or super-column
j E 7R, becomes the same as the pivot column pattern (Cj {r}) after the
symbolic update, it can be eliminated immediately. Since the columns [c]
represented by a super-column keep the same nonzero pattern until I'. :,- are
eliminated, we only need to maintain the degree, or other column selection
metric, for the representative column c.
With super-rows, the size of the set Cj differs from the sum of the numbers
of rows represented by the super-rows i in the set Cj. Similarly, IRi| is the
number of super-columns in row i, which is not the same as the number of
columns represented by the super-columns in Ri. Let IIRill denote the sum
of the numbers of rows represented by the super-columns j in ~i,

11 1- E d el|J ll

Similarly, we define \\Cj\ as

\c\ll= E [i]l.

Without super-columns, |Ri| and ||Rill are the same. Similarly, \Cj = \\Cj\
when there are no super-rows.
By necessity, the choice of the pivot column c is a heuristic, since obtaining
an ordering with minimum fill-in is an NP-complete problem [45]. Several
strategies are possible. Each selects a column c that minimizes some metric,
described below. For most of these methods, we need to compute the initial
metric for each column prior to symbolic factorization and ord. i, and then
recompute the metric for each column j in the pivot row pattern R, at step

1. Exact external row degree:
Select c to minimize the size of the resulting pivot row,

1,| (- U R \) } \ i

(We exclude the pivot column itself, thus computing an external row
degree [37].) The exact degree is expensive to compute (its time can
dominate the symbolic factorization time), and our experience with
symmetric orderings is that exact degrees do not provide better order-
ings than good approximate degrees (such as those from the ordering
methods amdbar and mc47bd) [1]. We thus did not test this method.

2. Matlab approximate external row degree:
The colmmd column ordering algorithm in Matlab is based on the sym-
bolic LU factorization algorithm presented in Section 3 [27]. Colmmd
selects as pivot the column c that minimizes a loose upper bound on
the external row degree,

I| ,|11 < Z (|lR \ {c}||).

Note that c E so I|| \ {c}|| = ||I- || |[c]|. Using this bound for
the symbolic update does not increase the ..-i-mptotic time complexity
of the ordering algorithm above that of the symbolic LU factorization
algorithm. Computing the Matlab metric for the initial columns of A
is very fast, taking only O(|A|) time.

3. AMD approximate external row degree:
The AMD algorithm for ordering symmetric matrices prior to a C'!i!. .1 -l:y
factorization is based on a bound on the external row degree that is
tighter than the Matlab bound [1]. It was first used in the nonsymmetric-
pattern multifrontal method (UMFPACK), to compute the ordering
during numerical factorization [8, 9]. In the context of a column or-
dering algorithm to bound the fill-in for the sparse partial pivoting
method, the bound on ||R,|| is

||7 | < 11|7I \ {c}|1 + E (11 i \7Z ), (3)
iEC0 4j

where R, is the most recent pivot row that modified C, in the symbolic
update, and thus s E C,. To compute the AMD metric on the initial
columns of A, we select an arbitrary s E Cc to compute the bound for
column c. Computing the initial AMD metric takes the same amount of
time as it takes to compute the sparse matrix product AAT. Although
this is costly, it does not increase the .i-, iii i1 ,tic time of our algorithm.
The time to compute this bound during the symbolic update phase is
..I- ,' !1l')1tically the same as computing the Matlab bound.

4. Size of the Householder update:
The symbolic LU factorization also computes the pattern of R for a QR
factorization of A [6, 32]. At step k, the size of the Householder update
is IL', I|-by-IIR,||. The term IL', I| is known exactly; the I||IRr term can
be computed or approximated using any of the above methods. The
method we tested selected c to minimize the product of I|Ccl| and the
AMD approximation of i|.R II in (3). We tested and then discarded this
method, since it gave much worse orderings than the other methods.

5. Approximate Markowitz criterion:
The Markowitz heuristic selects as pivot the entry aij that minimizes
the product of the degrees of row i and column j, which is the amount of
work required in the subsequent outer product step [38]. The criterion
assumes that the first k- 1 pivot rows and columns have been selected.
In our case, we do not know the exact pivot row ordering yet, so we do
not know the true row or column degrees. In our tests, we selected c to
minimize IICcl| times maxiec, I |Ri 1. This is a tighter upper bound on the

actual pivot row degree if column c is selected as the k-th pivot column
and row i is selected as the k-th pivot row. Although 7R does not
bound the pattern R, of the k-th pivot row for arbitrary row partial
pivoting, I||Ill does bound the size of the actual pivot row once the
row ordering is selected. Since I||Ccl and IIRill are known exactly, no
approximations need to be used. We tested and then discarded this
method, since it gave much worse orderings than the other methods.

6. Approximate deficiency:
The /7. fi., . ,/ of a pivot is the number of new nonzero entries that
would be created if that pivot is selected; selecting the pivot with least
deficiency leads to a minimum deficiency ordering algorithm. Exact
deficiency is very costly to compute. Approximate minimum deficiency
ordering algorithms have been successfully used for symmetric matrices,
in the context of ('!i. .1 -l:y factorization [40, 42]. In an nonsymmetric
context, the deficiency of column c can be bounded by

IlCclllIRI ( E I[i]I (l Rill- 11[C])

Any new nonzeros are limited to the ||Ccll-by-I|R,|| Householder up-
date. Each super-row i E Cc, however, is contained in this submatrix
and thus reduces the possible fill-in by the number of nonzeros it repre-
sents. The I||Ccl and IIRill terms are known exactly; we used the AMD
approximation for ||7r||, from (3).
We tested one variant of approximate deficiency [35, 36]. The initial-
ization phase computes the approximate deficiency of all columns, and
the approximate deficiency is recomputed any time a column is mod-
ified. No .,-.-ressive row absorption is used (discussed below), since
it worsens the approximation. The experimental results were mixed.
Compared to our final colamd variant, approximate deficiency was bet-
ter for about 1,i1' of the matrices in our large test suite. When it was
worse than colamd it was sometimes much worse, and vice versa.

As a by-product of the AMD row degree computation, we compute the
sizes of the set differences I|| \ R, I when super-row r is the bound on the
pivot row at step k, for all rows i remaining in the matrix. If we find that this
set difference is zero, Ri is a subset of row R,. The presence of row i does

not affect the exact row degree, although it does affect the Matlab and AMD
approximations. Row i can be deleted, and absorbed into [r]. We refer to
the deletion of 7R when j R, as aggressive row absorption. It is similar to
element absorption, introduced by Duff and Reid [17]. If AMD row degrees
are computed as the initial metric, then initial .,.-- ressive row absorption can
occur in that phase as well. Aggressive row absorption reduces the run time,
since it costs almost nothing to detect this condition when the AMD metric
is used, and results in fewer rows to consider in subsequent steps.
We tested sixteen variants of colamd, based on all possible combinations
of the following design decisions:

1. Initial metric: Matlab or AMD approximation.

2. Metric computed during the symbolic outer product update: Matlab
or AMD approximation.

3. With or without initial .,.---ressive row absorption.

4. With or without ..--reessive row absorption during the symbolic factor-

Note that some of the combinations are somewhat costly, since ..- -ressive
row absorption is easy when using the AMD row degrees, but difficult when
using the Matlab approximation.
Since the AMD metric was shown to be superior to the Matlab approx-
imation in the context of minimum degree orderings for sparse C'i. .! -l:y
factorization [1], we expected the four variants based solely on the AMD
metric to be superior, so much so that we did not intend to test all sixteen
methods. To our surprise, we found that better orderings were obtained with
an initial Matlab metric and an AMD metric during the symbolic update.
We discovered this by accident. A bug in our initial computation of the AMD
metric resulted in the Matlab approximation being computed instead. This
"b, L; gave us better orderings, so we kept it. Using an initial Matlab metric
gave, on average, orderings with f' fewer floating-point operations in the
subsequent numerical factorization than using an initial AMD metric. The
initial Matlab metric is also faster to compute.
The version of our ordering algorithm that we recommend, which we now
simply call colamd, uses the initial Matlab metric, the AMD metric for the
symbolic update, no initial..-.-.ressive row absorption, and ..-. -ressive row ab-
sorption during the symbolic update. Since the AMD metric is incompatible

with both incomplete degree update [18, 19] and multiple elimination [37],
it uses neither strategy. In multiple elimination, a set of pivotal columns is
selected such that the symbolic update of any column in the set does not
affect the other columns in the set. The selection of this set reduces the
number of symbolic updates that must be performed.
Matlab's colmmd ordering algorithm uses the Matlab metric throughout,
multiple elimination with a relaxed threshold, super-rows and super-columns,
mass elimination, and .r .- ressive row absorption. Aggressive row absorption
is not free, as it is in colamd. Instead, an explicit test is made every three
stages of multiple elimination. Super-columns are searched for every three
stages, by default. Rows more than 50' dense are ignored. Since the Matlab
metric can lead to lower quality orderings than an exact metric, options are
provided for selecting an exact external row degree metric, modifying the
multiple elimination threshold, changing the frequency of super-column de-
tection and ..-. -ressive row absorption, and changing the dense row threshold.
Selecting spparms ('tight') in Matlab improves the ordering computed by
colmmd, but at high cost in ordering time.

5 Experimental results

We tested our colamd ordering algorithm with three sets of matrices: square
nonsymmetric matrices, rectangular matrices, and symmetric positive defi-
nite matrices. Our test set is the entire University of Florida sparse matrix
collection [7], which includes the Harwell/Boeing test set [15, 16], the linear
programming problems in Netlib at http://www.netlib. org [13], as well as
many other matrices. We exclude complex matrices, nonsymmetric matrices
for which only the pattern was provided, and unassembled finite element ma-
trices. Some matrices include explicit zero entries in the description of their
pattern. Since we ignore numerical cancellation, we included these entries
when finding the ordering and determining the resulting factorization. Each
method is compared by ordering time and quality (number of nonzeros in
the factors, and number of floating-point operations to compute the factor-
ization). Although we tested all matrices in the collection, we present here
a summary of only some of the larger problems (those for which the best
ordering resulted in a factorization requiring 107 operations or more to com-
pute). Complete results are presented in Larimore's thesis [36], available as a
technical report at http://www.cise.uf We performed these exper-

iments on a Sun Ultra Enterprise 4000/5000 with 2 GB of main memory and
eight 248 Mhz UltraSparc-II processors (only one processor was used). The
code was written in ANSI/ISO C and compiled using the Solaris C compiler
(via Mathwork's mex shell script for interfacing Matlab to software written
in C), with strict ANSI/ISO compliance.

5.1 Square nonsymmetric matrices

For square nonsymmetric matrices, we used colamd to find a column pre-
ordering Q for sparse partial pivoting. These results are compared with
colmmd with default option settings, and with amdbar [1] applied to the pat-
tern of ATA (ignoring numerical cancellation, and withholding the same
dense rows and columns from A that were ignored by colamd and colmmd).
The amdbar routine is the same as mc47bd in the Harwell Subroutine Library,
except that it does not perform ..-:- ressive row absorption. Both amdbar and
mc47bd use the AMD--I *,1.. approximate degree. For sparse partial pivoting
with the matrices in our test set, amdbar provides slightly better orderings
than mc47bd. After finding a column ordering, we factorized the matrix AQ
with the SuperLU package [11]. This package was chosen since colamd was
written to replace the column ordering in the current version of SuperLU.
SuperLU is based on the BLAS [12].2
We excluded matrices that can be permuted to upper block triangular
form [14] with a substantial improvement in factorization time, for two rea-
sons: (1) our bounds are not tight if the matrix A is not strong Hall, and (2)
SuperLU does not take advantage of reducibility to block upper triangular
form. Such matrices should be factorized by ordering and factorizing each
irreducible diagonal submatrix, after permuting the matrix to block triangu-
lar form. Our resulting test set had 106 matrices, all requiring more than
107 or more operations to factorize.
A representative sample is shown in in Table 1, sorted according to colamd
versus colmmd ordering quality.3 Table 2 reports the ordering time of colamd,

2We used the BLAS routines provided in SuperLU because factorization time is a
secondary measure, and because we had difficulty using the Sun-optimized BLAS from a
Matlab-callable driver.
3To obtain the sample, we sorted the 106 matrices according to the relative floating-
point operation count for the colamd ordering and the colmmd ordering, and then selected
every 8th matrix. We also included TWOTONE, the matrix with the largest dimension in
the test set.

Table 1: Unsymmetric matrices
matrix n nnz description
GOODWIN 7320 324784 finite-element, fluid dynamics, Navier-Stokes
and elliptic mesh (R. Goodwin)
RAEFSKY2 3242 294276 incompressible flow in pressure-driven pipe
(A. Raefsky)
TWOTONE 120750 1224224 frequency-domain .111 i-,-i, of nonlinear
analog circuit [20]
RMA10 !I, ; 2374001 3D computational fluid dynamics (CFD),
C'li ,i -Ion Harbor (Steve Bova)
LHR17C 17576 381975 light hydrocarbon recovery problem [47]
AF23560 23560 !2'., airfoil [3]
EPB2 25228 175027 plate-fin heat exchanger with flow
redistribution (D. Averous)
RDIST3A 2398 61896 chemical process separation [46]
GARON2 13535 390607 2D finite-element, Navier-Stokes (A. Garon)
GRAHAM1 9035 335504 Galerkin finite-element, Navier-Stokes,
two-phase fluid flow (D. Graham)
CAVITY25 4562 138187 driven cavity, finite-element (A. Chapman)
Ex20 2203 69981 2D attenuation of a surface disturbance,
nonlinear CFD (Y. Saad)
WANG1 2903 19093 electron continuity, 3D diode [39]
EX14 3251 66775 2D isothermal seepage flow (Y. Saad)
Ex40 7740 458012 3D die swell problem on a square die,
computational fluid dynamics (Y. Saad)

colmmd, and amdbar (the time to compute the pattern of ATA is included
in the amdbar time). For comparison, the last column reports the SuperLU
factorization time, using the colamd ordering. Table 3 gives the resulting
number of nonzeros in L + U, and the floating-point operations required to
factorize the permuted matrix, for each of the ordering methods. The median
results reported in this paper are for just the representative samples, not the
full test sets. The results for the samples and the full test sets are similar.
Colamd is superior to the other methods for these matrices. Colamd was
typically 3.9 times faster than colmmd and 2.1 times faster than amdbar. For
two matrices, colmmd took more time to order the matrix than SuperLU took
to factorize it. The orderings found by colmmd result in a median increase of

Table 2: Ordering and factorization time, in seconds
matrix colamd colmmd amdbar SuperLU
GOODWIN 0.31 0.42 1.51 44.65
RAEFSKY2 1.11 1.58 2.11 62.36
TWOTONE 5.88 70.82 14.15 306.85
RMA10 2.37 25.71 12.07 120.67
LHR17C 1.62 15.22 3.08 5.80
AF23560 6.64 115.99 1.72 108.35
EPB2 0.44 2.62 0.61 16.32
RDIST3A 0.03 0.07 0.21 0.44
GARON2 0.61 1.19 1.29 25.12
GRAHAM1 0.43 0.58 1.50 31.63
CAVITY25 0.12 0.21 0.46 2.95
EX20 0.08 0.17 0.24 1.13
WANG1 0.07 0.27 0.08 2.65
EX14 0.10 0.53 0.20 2.47
EX40 2.34 13.58 2.61 27.21
Median time
relative to 3.9 2.1 37.1

Table 3: Ordering quality, as factorized by SuperLU
Nonzeros in L + U (103) Flop. count (106)
matrix colamd colmmd amdbar colamd colmmd amdbar
GOODWIN 1i 3103 2734 1909 l". 498
RAEFSKY2 ;i 1l 3236 2877 2374 2012 1682
TWOTONE 21030 19624 21280 Is 8923 8578
RMA10 1 ", 10 17544 15624 5064 5303 4041
LHR17C 1715 1767 1717 111 120 112
AF23560 12110 13333 13131 4515 5350 5052
EPB2 3186 3547 3135 !11. 839 620
RDIST3A 233 264 231 11 15 11
GARON2 5179 5120 5128 1061 1563 1505
GRAHAM1 4920 5344 4518 1333 2115 1414
CAVITY25 1146 1347 1210 130 219 178
EX20 483 589 464 48 86 50
WANG1 632 868 752 120 242 174
EX14 711 1012 91 214 157
EX40 4315 8537 6072 1075 5369 2606
Median result
relative to 1.10 0.99 1.36 1.05

10' in nonzeros in the LU factors and :''. in floating-point operations, as
compared to colamd. The ordering quality of colamd and amdbar are similar,
although there are large variations in both directions in a small number of
matrices. For a few matrices (in our larger test set, not in Table 1) amdbar
requires more space to store ATA than SuperLU requires to factorize the
permuted matrix.

5.2 Rectangular matrices

For m-bi-,, rectangular matrices with m > n, we found a column ordering
Q for the Cholesky factorization of (AQ)T(AQ), which is one method for
solving a least squares problem. If m < n, we found a row ordering P for
the Cholesky factorization of (PA)D2(PA)T, which arises in interior point
methods for solving linear programming problems [34, 44]. Here, D is a
diagonal matrix. In the latter case, colamd and colmmd found a column
ordering of AT and we used that as the row ordering P. We compared these
two methods with amdbar on the corresponding matrix, ATA (if m > n) or
AAT (if m < n).
Our test set was limited. It included only 37 matrices requiring more
than 107 operations; all but three of these were Netlib linear programming
problems (with m < n). A representative selection4 is shown in Table 4.
We compared the three methods based on their ordering time, number
of nonzeros in the factor L, and floating-point operation count required for
the Cholesky factorization. These metrics were obtained from symbfact in
Matlab, a fast symbolic factorization [22, 23, 29]. We did not perform the
numerical Cholesky factorization. The time to construct ATA or AAT is
included in the ordering time for amdbar. The results are shown in Ta-
bles 5 and 6.
For these matrices, colamd was twice as fast as colmmd and slightly faster
than amdbar. All three produced comparable orderings. Each method has a
few matrices it does well on, and a few that it does poorly on. Colamd and
colmmd both typically require less storage than amdbar, sometimes by several
orders of magnitude, because of the need to compute the pattern of AAT
prior to ordering it with amdbar. The size of the Cholesky factors alk--w;
dominates the size of AAT, however. The primary advantage of colamd and
'L i 5th matrix from the 37 large matrices, in increasing order of colamd versus
colmmd ordering quality, was chosen.

Table 4: Rectangular matrices (all linear programming problems)

matrix m n nnz nnz(AA 1) description
GRAN 2629 2525 20111 60511 British Petroleum operations
NUGO8 912 1632 7296 28816 LP lower bound for a
quadratic assignment problem [41]
FITIP 627 1677 '- 274963 fitting linear inequalities to
data, min. sum of piecewise-
linear penalties (R. Fourer)
KLEIN2 477 531 5062 1:;' "', (E. Klotz)
PILOT87 2030 6680 74949 238624 (J. Stone)
PDS_20 33874 108175 232647 320196 military airlift operations [5]
D2Q06C 2171 5831 33081 56153 (J. Tomlin)

Table 5: Ordering time, in seconds
matrix colamd colmmd amdbar
GRAN 0.04 0.07 0.06
NUGO8 0.05 0.06 0.04
FITIP 0.01 0.01 0.08
KLEIN2 0.01 0.02 0.07
PILOT87 0.56 2.60 0.52
PDS_20 3.86 10.53 3.92
D2Q06C 0.07 0.44 0.08
Median time
relative to 2.00 1.14

Table 6: Ordering quality
Nonzeros in L (103) Flop. count (106)
matrix colamd colmmd amdbar colamd colmmd amdbar
GRAN 191 158 143 48 33 27
NUG08 210 202 230 78 70 92
FIT1P 197 197 197 82 82 82
KLEIN2 81 82 80 17 18 17
PILOT87 423 475 406 169 195 164
PDS_20 6827 8159 6929 ".' 12640 8812
D2Q06C 175 271 147 35 97 27
Median result
relative to 1.00 0.99 1.02 0.98

colmmd over amdbar in this context is the ability to analyze the C'!i.!, -l:y
factorization by finding the ordering and size of the resulting factor L in
space proportional to the number of nonzeros in the matrix A, rather than
proportional to the size of the matrix to be factorized (ATA or AAT).

5.3 Symmetric matrices

For symmetric matrices, Matlab's symmmd routine constructs a matrix M
such that the pattern of MTM is the same as A, and then finds a column
ordering of M using colmmd. There is one row in M for each entry ai below
the diagonal of A, with nonzero entries in column i and j. This method gives
a reasonable ordering for the Cholesky factorization of A. We implemented
an analogous symamd routine that is based on colamd.
Our test set included 50 matrices requiring 107 or more operations to
factorize. The largest was a computational fluid dynamics problem from Ed
Rothberg requiring 136 billion operations to factorize (cfd2). Our sample
test matrices5 are shown in Table 7. Results from symamd, symmmd, amdbar,
and mc47bd are shown in Tables 8 and 9. The time to construct M is included
in the ordering time for symamd and symmmd.
For these 9 matrices, symamd was over six times faster than symmmd, on

5Every 7th matrix, in order of increasing ratio of symamd to symmmd ordering quality,
was chosen.

Table 7: Symmetric matrices

Table 8: Ordering time, in seconds
matrix symamd symmmd amdbar mc47bd
PWT 0.78 3.53 0.45 0.44
MSC00726 0.05 0.24 0.02 0.02
BC-- iI. :S 0.53 22.45 0.12 0.11
NASA2146 0.08 0.49 0.02 0.02
CFD2 7.36 48.68 3.90 3.71
3DTUBE 6.09 77.75 0.95 0.92
GEARBOX 16.58 454.49 2.94 2.89
CRYSTM02 0.71 1.36 0.28 0.28
FINAN512 1.60 2.61 0.92 0.95
Median time
relative to 6.13 0.39 0.39

matrix n nnz description
PWT 36519 326107 pressurized wind tunnel (R. Grimes)
MSC00726 726 34518 symmetric test matrix from \ ISC/NASTRAN
BC'-- II. : 8032 :;". ~.111 stiffness matrix, airplane engine component (R. Grimes)
NASA2146 2146 72250 structural engineering problem (NASA Langley)
CFD2 123440 3087898 computational fluid dynamics (E. Rothberg)
3DTUBE 45330 3213618 3D pressure tube (E. Rothberg)
GEARBOX 153746 9080404 ZF aircraft flap actuator (E. Rothberg)
CRYSTM02 1:;' 322905 crystal, free vibration mass matrix (R. Grimes)
FINAN512 74752 ".',' ,'2 portfolio optimization [4]

Table 9: Ordering quality

Nonzeros in L (103) C'!. -!; flop. count (106)
matrix symamd symmmd amdbar mc47bd symamd symmmd amdbar mc47bd
PWT 1497 1425 1592 1556 156 140 173 162
MSC00726 103 108 111 111 20 22 23 23
BC(-- I. :S 735 803 752 737 118 143 127 119
NASA2146 135 157 140 140 11 15 12 12
CFD2 74832 94444 75008 75008 137470 211328 1:;, 171. 1:;, 71'.
3DTUBE 26128 33213 _i,;',, i,;,', 45578 30053 30053
GEARBOX 49268 63940 !i.'.. 1''' 49849 -1,.16 47068 47067
CRYSTM02 2025 3159 2286 2286 546 1240 719 719
FINAN512 2028 8223 2851 2838 249 12356 644 633
Median result
relative to 1.26 1.03 1.03 1.52 1.08 1.04

average. It nearly alv--l;- produced significantly better orderings. In con-
trast, symamd was al--iv slower than amdbar and mc47bd, although it found
orderings of similar quality (the FINAN512 is a notable exception, but none
of these methods finds as good an ordering as a tree dissection method [4]).
An ordering algorithm designed for symmetric matrices (amdbar or mc47bd)
is thus superior to one based on a column ordering of M. There may be at
least one important exception, however. In some applications, a better ma-
trix M is available. Consider an n-bi--,, finite element matrix constructed as
the summation of e finite elements, where normally e < n. Each finite ele-
ment matrix is a dense symmetric submatrix. Suppose element k is nonzero
for all entries ai for which both i and j are in the set Sk. We can construct
a e-bi--, matrix M, where row k has the pattern Sk. Since MTM has the
same pattern as A, we can compute a column ordering of M and use it for
the Cholesky factorization of A. Colamd would be faster than when using an
M constructed without the knowledge of the finite element structure. The
space to perform the ordering and symbolic analysis is less as well, since M
has fewer nonzeros than A. Although 1n in: of the matrices in our test set
arise from finite element problems, only a few small ones are available in
unassembled form, as a collection of finite elements. Thus, we are not able
to evaluate this strategy on large, realistic test matrices.

6 Summary

Two new ordering routines, colamd and symamd, have been presented. For
square nonsymmetric matrices, colamd is much faster and provides better or-
derings than Matlab's colmmd routine. It is also faster than symmetric-based
ordering methods (such as amdbar), and uses less storage. For rectangular
matrices (such as those arising in least squares problems and interior point
methods for linear programming), colamd is faster than colmmd and amdbar
and finds orderings of comparable quality. We presented a symmetric order-
ing method symamd based on colamd; although it produces orderings as good
as a truly symmetric ordering algorithm (amdbar), it is slower than amdbar.
The colamd and symamd routines are written in ANSI/ISO C, with Matlab-
callable interfaces. Version 2.0 of the code is freely available from the follow-
ing sources:
1. University of Florida, http://www.cise.ufl. edu/research/sparse.

2. Netlib,

3. The MathWorks, Inc., for user-contributed contributions to Matlab, Colamd and symamd are built-in func-
tions in Matlab Version 6.0.

4. The collected algorithms of the AC\ I, as Algorithm 8xx, described in


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