(octave.info)Mathematical Considerations
22.1.4.3 Mathematical Considerations
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The attempt has been made to make sparse matrices behave in exactly the
same manner as there full counterparts. However, there are certain
differences and especially differences with other products sparse
implementations.
First, the "./" and ".^" operators must be used with care. Consider
what the examples
s = speye (4);
a1 = s .^ 2;
a2 = s .^ s;
a3 = s .^ -2;
a4 = s ./ 2;
a5 = 2 ./ s;
a6 = s ./ s;
will give. The first example of S raised to the power of 2 causes no
problems. However S raised element-wise to itself involves a large
number of terms ‘0 .^ 0’ which is 1. There ‘S .^ S’ is a full matrix.
Likewise ‘S .^ -2’ involves terms like ‘0 .^ -2’ which is infinity,
and so ‘S .^ -2’ is equally a full matrix.
For the "./" operator ‘S ./ 2’ has no problems, but ‘2 ./ S’ involves
a large number of infinity terms as well and is equally a full matrix.
The case of ‘S ./ S’ involves terms like ‘0 ./ 0’ which is a ‘NaN’ and
so this is equally a full matrix with the zero elements of S filled with
‘NaN’ values.
The above behavior is consistent with full matrices, but is not
consistent with sparse implementations in other products.
A particular problem of sparse matrices comes about due to the fact
that as the zeros are not stored, the sign-bit of these zeros is equally
not stored. In certain cases the sign-bit of zero is important. For
example:
a = 0 ./ [-1, 1; 1, -1];
b = 1 ./ a
⇒ -Inf Inf
Inf -Inf
c = 1 ./ sparse (a)
⇒ Inf Inf
Inf Inf
To correct this behavior would mean that zero elements with a
negative sign-bit would need to be stored in the matrix to ensure that
their sign-bit was respected. This is not done at this time, for
reasons of efficiency, and so the user is warned that calculations where
the sign-bit of zero is important must not be done using sparse
matrices.
In general any function or operator used on a sparse matrix will
result in a sparse matrix with the same or a larger number of nonzero
elements than the original matrix. This is particularly true for the
important case of sparse matrix factorizations. The usual way to
address this is to reorder the matrix, such that its factorization is
sparser than the factorization of the original matrix. That is the
factorization of ‘L * U = P * S * Q’ has sparser terms ‘L’ and ‘U’ than
the equivalent factorization ‘L * U = S’.
Several functions are available to reorder depending on the type of
the matrix to be factorized. If the matrix is symmetric
positive-definite, then “symamd” or “csymamd” should be used. Otherwise
“amd”, “colamd” or “ccolamd” should be used. For completeness the
reordering functions “colperm” and “randperm” are also available.
Note: Figure 22.2, for an example of the structure
of a simple positive definite matrix.
[image src="spmatrix.png" text="
| * *
| * * * *
| * * * *
| * * * *
5 - * * * *
| * * * *
| * * * *
| * * *
| * *
10 - * *
| * *
| * *
| * *
| * *
15 - * *
|----------|---------|---------|
5 10 15" ]
Figure 22.2: Structure of simple sparse matrix.
The standard Cholesky factorization of this matrix can be obtained by
the same command that would be used for a full matrix. This can be
visualized with the command ‘r = chol (A); spy (r);’. Note: Figure
22.3. The original matrix had 43 nonzero terms, while
this Cholesky factorization has 71, with only half of the symmetric
matrix being stored. This is a significant level of fill in, and
although not an issue for such a small test case, can represents a large
overhead in working with other sparse matrices.
The appropriate sparsity preserving permutation of the original
matrix is given by “symamd” and the factorization using this reordering
can be visualized using the command ‘q = symamd (A); r = chol (A(q,q));
spy (r)’. This gives 29 nonzero terms which is a significant
improvement.
The Cholesky factorization itself can be used to determine the
appropriate sparsity preserving reordering of the matrix during the
factorization, In that case this might be obtained with three return
arguments as ‘[r, p, q] = chol (A); spy (r)’.
[image src="spchol.png" text="
| * *
| * * *
| * * * *
| * * * * *
5 - * * * * * *
| * * * * * * *
| * * * * * * * *
| * * * * * * * *
| * * * * * * *
10 - * * * * * *
| * * * * *
| * * * *
| * * *
| * *
15 - *
|----------|---------|---------|
5 10 15" ]
Figure 22.3: Structure of the unpermuted Cholesky factorization of the
above matrix.
[image src="spcholperm.png" text="
| * *
| * *
| * *
| * *
5 - * *
| * *
| * *
| * *
| * *
10 - * *
| * *
| * *
| * *
| * *
15 - *
|----------|---------|---------|
5 10 15" ]
Figure 22.4: Structure of the permuted Cholesky factorization of the
above matrix.
In the case of an asymmetric matrix, the appropriate sparsity
preserving permutation is “colamd” and the factorization using this
reordering can be visualized using the command ‘q = colamd (A); [l, u,
p] = lu (A(:,q)); spy (l+u)’.
Finally, Octave implicitly reorders the matrix when using the div (/)
and ldiv (\) operators, and so no the user does not need to explicitly
reorder the matrix to maximize performance.
-- : P = amd (S)
-- : P = amd (S, OPTS)
Return the approximate minimum degree permutation of a matrix.
This is a permutation such that the Cholesky factorization of ‘S
(P, P)’ tends to be sparser than the Cholesky factorization of S
itself. ‘amd’ is typically faster than ‘symamd’ but serves a
similar purpose.
The optional parameter OPTS is a structure that controls the
behavior of ‘amd’. The fields of the structure are
OPTS.dense
Determines what ‘amd’ considers to be a dense row or column of
the input matrix. Rows or columns with more than ‘max (16,
(dense * sqrt (N)))’ entries, where N is the order of the
matrix S, are ignored by ‘amd’ during the calculation of the
permutation. The value of dense must be a positive scalar and
the default value is 10.0
OPTS.aggressive
If this value is a nonzero scalar, then ‘amd’ performs
aggressive absorption. The default is not to perform
aggressive absorption.
The author of the code itself is Timothy A. Davis (see
<http://faculty.cse.tamu.edu/davis/suitesparse.html>).
See also: Note: symamd, Note: colamd.
-- : P = ccolamd (S)
-- : P = ccolamd (S, KNOBS)
-- : P = ccolamd (S, KNOBS, CMEMBER)
-- : [P, STATS] = ccolamd (...)
Constrained column approximate minimum degree permutation.
‘P = ccolamd (S)’ returns the column approximate minimum degree
permutation vector for the sparse matrix S. For a non-symmetric
matrix S, ‘S(:, P)’ tends to have sparser LU factors than S. ‘chol
(S(:, P)' * S(:, P))’ also tends to be sparser than ‘chol (S' *
S)’. ‘P = ccolamd (S, 1)’ optimizes the ordering for ‘lu (S(:,
P))’. The ordering is followed by a column elimination tree
post-ordering.
KNOBS is an optional 1-element to 5-element input vector, with a
default value of ‘[0 10 10 1 0]’ if not present or empty. Entries
not present are set to their defaults.
‘KNOBS(1)’
if nonzero, the ordering is optimized for ‘lu (S(:, p))’. It
will be a poor ordering for ‘chol (S(:, P)' * S(:, P))’. This
is the most important knob for ccolamd.
‘KNOBS(2)’
if S is m-by-n, rows with more than ‘max (16, KNOBS(2) * sqrt
(n))’ entries are ignored.
‘KNOBS(3)’
columns with more than ‘max (16, KNOBS(3) * sqrt (min (M,
N)))’ entries are ignored and ordered last in the output
permutation (subject to the cmember constraints).
‘KNOBS(4)’
if nonzero, aggressive absorption is performed.
‘KNOBS(5)’
if nonzero, statistics and knobs are printed.
CMEMBER is an optional vector of length n. It defines the
constraints on the column ordering. If ‘CMEMBER(j) = C’, then
column J is in constraint set C (C must be in the range 1 to n).
In the output permutation P, all columns in set 1 appear first,
followed by all columns in set 2, and so on. ‘CMEMBER = ones
(1,n)’ if not present or empty. ‘ccolamd (S, [], 1 : n)’ returns
‘1 : n’
‘P = ccolamd (S)’ is about the same as ‘P = colamd (S)’. KNOBS and
its default values differ. ‘colamd’ always does aggressive
absorption, and it finds an ordering suitable for both ‘lu (S(:,
P))’ and ‘chol (S(:, P)' * S(:, P))’; it cannot optimize its
ordering for ‘lu (S(:, P))’ to the extent that ‘ccolamd (S, 1)’
can.
STATS is an optional 20-element output vector that provides data
about the ordering and the validity of the input matrix S.
Ordering statistics are in ‘STATS(1 : 3)’. ‘STATS(1)’ and
‘STATS(2)’ are the number of dense or empty rows and columns
ignored by CCOLAMD and ‘STATS(3)’ is the number of garbage
collections performed on the internal data structure used by
CCOLAMD (roughly of size ‘2.2 * nnz (S) + 4 * M + 7 * N’ integers).
‘STATS(4 : 7)’ provide information if CCOLAMD was able to continue.
The matrix is OK if ‘STATS(4)’ is zero, or 1 if invalid.
‘STATS(5)’ is the rightmost column index that is unsorted or
contains duplicate entries, or zero if no such column exists.
‘STATS(6)’ is the last seen duplicate or out-of-order row index in
the column index given by ‘STATS(5)’, or zero if no such row index
exists. ‘STATS(7)’ is the number of duplicate or out-of-order row
indices. ‘STATS(8 : 20)’ is always zero in the current version of
CCOLAMD (reserved for future use).
The authors of the code itself are S. Larimore, T. Davis and S.
Rajamanickam in collaboration with J. Bilbert and E. Ng. Supported
by the National Science Foundation (DMS-9504974, DMS-9803599,
CCR-0203270), and a grant from Sandia National Lab. See
<http://faculty.cse.tamu.edu/davis/suitesparse.html> for ccolamd,
csymamd, amd, colamd, symamd, and other related orderings.
See also: Note: colamd, Note: csymamd.
-- : P = colamd (S)
-- : P = colamd (S, KNOBS)
-- : [P, STATS] = colamd (S)
-- : [P, STATS] = colamd (S, KNOBS)
Compute the column approximate minimum degree permutation.
‘P = colamd (S)’ returns the column approximate minimum degree
permutation vector for the sparse matrix S. For a non-symmetric
matrix S, ‘S(:,P)’ tends to have sparser LU factors than S. The
Cholesky factorization of ‘S(:,P)' * S(:,P)’ also tends to be
sparser than that of ‘S' * S’.
KNOBS is an optional one- to three-element input vector. If S is
m-by-n, then rows with more than ‘max(16,KNOBS(1)*sqrt(n))’ entries
are ignored. Columns with more than ‘max
(16,KNOBS(2)*sqrt(min(m,n)))’ entries are removed prior to
ordering, and ordered last in the output permutation P. Only
completely dense rows or columns are removed if ‘KNOBS(1)’ and
‘KNOBS(2)’ are < 0, respectively. If ‘KNOBS(3)’ is nonzero, STATS
and KNOBS are printed. The default is ‘KNOBS = [10 10 0]’. Note
that KNOBS differs from earlier versions of colamd.
STATS is an optional 20-element output vector that provides data
about the ordering and the validity of the input matrix S.
Ordering statistics are in ‘STATS(1:3)’. ‘STATS(1)’ and ‘STATS(2)’
are the number of dense or empty rows and columns ignored by COLAMD
and ‘STATS(3)’ is the number of garbage collections performed on
the internal data structure used by COLAMD (roughly of size ‘2.2 *
nnz(S) + 4 * M + 7 * N’ integers).
Octave built-in functions are intended to generate valid sparse
matrices, with no duplicate entries, with ascending row indices of
the nonzeros in each column, with a non-negative number of entries
in each column (!) and so on. If a matrix is invalid, then COLAMD
may or may not be able to continue. If there are duplicate entries
(a row index appears two or more times in the same column) or if
the row indices in a column are out of order, then COLAMD can
correct these errors by ignoring the duplicate entries and sorting
each column of its internal copy of the matrix S (the input matrix
S is not repaired, however). If a matrix is invalid in other ways
then COLAMD cannot continue, an error message is printed, and no
output arguments (P or STATS) are returned. COLAMD is thus a
simple way to check a sparse matrix to see if it’s valid.
‘STATS(4:7)’ provide information if COLAMD was able to continue.
The matrix is OK if ‘STATS(4)’ is zero, or 1 if invalid.
‘STATS(5)’ is the rightmost column index that is unsorted or
contains duplicate entries, or zero if no such column exists.
‘STATS(6)’ is the last seen duplicate or out-of-order row index in
the column index given by ‘STATS(5)’, or zero if no such row index
exists. ‘STATS(7)’ is the number of duplicate or out-of-order row
indices. ‘STATS(8:20)’ is always zero in the current version of
COLAMD (reserved for future use).
The ordering is followed by a column elimination tree
post-ordering.
The authors of the code itself are Stefan I. Larimore and Timothy
A. Davis. The algorithm was developed in collaboration with John
Gilbert, Xerox PARC, and Esmond Ng, Oak Ridge National Laboratory.
(see <http://faculty.cse.tamu.edu/davis/suitesparse.html>)
See also: Note: colperm, Note: symamd,
Note: ccolamd.
-- : P = colperm (S)
Return the column permutations such that the columns of ‘S (:, P)’
are ordered in terms of increasing number of nonzero elements.
If S is symmetric, then P is chosen such that ‘S (P, P)’ orders the
rows and columns with increasing number of nonzeros elements.
-- : P = csymamd (S)
-- : P = csymamd (S, KNOBS)
-- : P = csymamd (S, KNOBS, CMEMBER)
-- : [P, STATS] = csymamd (...)
For a symmetric positive definite matrix S, return the permutation
vector P such that ‘S(P,P)’ tends to have a sparser Cholesky factor
than S.
Sometimes ‘csymamd’ works well for symmetric indefinite matrices
too. The matrix S is assumed to be symmetric; only the strictly
lower triangular part is referenced. S must be square. The
ordering is followed by an elimination tree post-ordering.
KNOBS is an optional 1-element to 3-element input vector, with a
default value of ‘[10 1 0]’. Entries not present are set to their
defaults.
‘KNOBS(1)’
If S is n-by-n, then rows and columns with more than
‘max(16,KNOBS(1)*sqrt(n))’ entries are ignored, and ordered
last in the output permutation (subject to the cmember
constraints).
‘KNOBS(2)’
If nonzero, aggressive absorption is performed.
‘KNOBS(3)’
If nonzero, statistics and knobs are printed.
CMEMBER is an optional vector of length n. It defines the
constraints on the ordering. If ‘CMEMBER(j) = S’, then row/column
j is in constraint set C (C must be in the range 1 to n). In the
output permutation P, rows/columns in set 1 appear first, followed
by all rows/columns in set 2, and so on. ‘CMEMBER = ones (1,n)’ if
not present or empty. ‘csymamd (S,[],1:n)’ returns ‘1:n’.
‘P = csymamd (S)’ is about the same as ‘P = symamd (S)’. KNOBS and
its default values differ.
‘STATS(4:7)’ provide information if CCOLAMD was able to continue.
The matrix is OK if ‘STATS(4)’ is zero, or 1 if invalid.
‘STATS(5)’ is the rightmost column index that is unsorted or
contains duplicate entries, or zero if no such column exists.
‘STATS(6)’ is the last seen duplicate or out-of-order row index in
the column index given by ‘STATS(5)’, or zero if no such row index
exists. ‘STATS(7)’ is the number of duplicate or out-of-order row
indices. ‘STATS(8:20)’ is always zero in the current version of
CCOLAMD (reserved for future use).
The authors of the code itself are S. Larimore, T. Davis and S.
Rajamanickam in collaboration with J. Bilbert and E. Ng. Supported
by the National Science Foundation (DMS-9504974, DMS-9803599,
CCR-0203270), and a grant from Sandia National Lab. See
<http://faculty.cse.tamu.edu/davis/suitesparse.html> for ccolamd,
colamd, csymamd, amd, colamd, symamd, and other related orderings.
See also: Note: symamd, Note: ccolamd.
-- : P = dmperm (S)
-- : [P, Q, R, S] = dmperm (S)
Perform a Dulmage-Mendelsohn permutation of the sparse matrix S.
With a single output argument ‘dmperm’ performs the row
permutations P such that ‘S(P,:)’ has no zero elements on the
diagonal.
Called with two or more output arguments, returns the row and
column permutations, such that ‘S(P, Q)’ is in block triangular
form. The values of R and S define the boundaries of the blocks.
If S is square then ‘R == S’.
The method used is described in: A. Pothen & C.-J. Fan. ‘Computing
the Block Triangular Form of a Sparse Matrix’. ACM Trans. Math.
Software, 16(4):303-324, 1990.
See also: Note: colamd, Note: ccolamd.
-- : P = symamd (S)
-- : P = symamd (S, KNOBS)
-- : [P, STATS] = symamd (S)
-- : [P, STATS] = symamd (S, KNOBS)
For a symmetric positive definite matrix S, returns the permutation
vector p such that ‘S(P, P)’ tends to have a sparser
Cholesky factor than S.
Sometimes ‘symamd’ works well for symmetric indefinite matrices
too. The matrix S is assumed to be symmetric; only the strictly
lower triangular part is referenced. S must be square.
KNOBS is an optional one- to two-element input vector. If S is
n-by-n, then rows and columns with more than ‘max
(16,KNOBS(1)*sqrt(n))’ entries are removed prior to ordering, and
ordered last in the output permutation P. No rows/columns are
removed if ‘KNOBS(1) < 0’. If ‘KNOBS(2)’ is nonzero, STATS and
KNOBS are printed. The default is ‘KNOBS = [10 0]’. Note that
KNOBS differs from earlier versions of ‘symamd’.
STATS is an optional 20-element output vector that provides data
about the ordering and the validity of the input matrix S.
Ordering statistics are in ‘STATS(1:3)’. ‘STATS(1) = STATS(2)’ is
the number of dense or empty rows and columns ignored by SYMAMD and
‘STATS(3)’ is the number of garbage collections performed on the
internal data structure used by SYMAMD (roughly of size ‘8.4 * nnz
(tril (S, -1)) + 9 * N’ integers).
Octave built-in functions are intended to generate valid sparse
matrices, with no duplicate entries, with ascending row indices of
the nonzeros in each column, with a non-negative number of entries
in each column (!) and so on. If a matrix is invalid, then SYMAMD
may or may not be able to continue. If there are duplicate entries
(a row index appears two or more times in the same column) or if
the row indices in a column are out of order, then SYMAMD can
correct these errors by ignoring the duplicate entries and sorting
each column of its internal copy of the matrix S (the input matrix
S is not repaired, however). If a matrix is invalid in other ways
then SYMAMD cannot continue, an error message is printed, and no
output arguments (P or STATS) are returned. SYMAMD is thus a
simple way to check a sparse matrix to see if it’s valid.
‘STATS(4:7)’ provide information if SYMAMD was able to continue.
The matrix is OK if ‘STATS (4)’ is zero, or 1 if invalid.
‘STATS(5)’ is the rightmost column index that is unsorted or
contains duplicate entries, or zero if no such column exists.
‘STATS(6)’ is the last seen duplicate or out-of-order row index in
the column index given by ‘STATS(5)’, or zero if no such row index
exists. ‘STATS(7)’ is the number of duplicate or out-of-order row
indices. ‘STATS(8:20)’ is always zero in the current version of
SYMAMD (reserved for future use).
The ordering is followed by a column elimination tree
post-ordering.
The authors of the code itself are Stefan I. Larimore and Timothy
A. Davis. The algorithm was developed in collaboration with John
Gilbert, Xerox PARC, and Esmond Ng, Oak Ridge National Laboratory.
(see <http://faculty.cse.tamu.edu/davis/suitesparse.html>)
See also: Note: colperm, Note: colamd.
-- : P = symrcm (S)
Return the symmetric reverse Cuthill-McKee permutation of S.
P is a permutation vector such that ‘S(P, P)’ tends to have its
diagonal elements closer to the diagonal than S. This is a good
preordering for LU or Cholesky factorization of matrices that come
from “long, skinny” problems. It works for both symmetric and
asymmetric S.
The algorithm represents a heuristic approach to the NP-complete
bandwidth minimization problem. The implementation is based in the
descriptions found in
E. Cuthill, J. McKee. ‘Reducing the Bandwidth of Sparse Symmetric
Matrices’. Proceedings of the 24th ACM National Conference,
157–172 1969, Brandon Press, New Jersey.
A. George, J.W.H. Liu. ‘Computer Solution of Large Sparse Positive
Definite Systems’, Prentice Hall Series in Computational
Mathematics, ISBN 0-13-165274-5, 1981.
See also: Note: colperm, Note: colamd,
Note: symamd.
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