\documentclass[11pt]{article} \batchmode \newcommand{\m}[1]{{\bf{#1}}} % for matrices and vectors \newcommand{\tr}{^{\sf T}} % transpose \topmargin 0in \textheight 9in \oddsidemargin 0pt \evensidemargin 0pt \textwidth 6.5in %------------------------------------------------------------------------------ \begin{document} %------------------------------------------------------------------------------ \title{AMD User Guide} \author{Patrick R. Amestoy\thanks{ENSEEIHT-IRIT, 2 rue Camichel 31017 Toulouse, France. email: amestoy@enseeiht.fr. http://www.enseeiht.fr/$\sim$amestoy.} \and Timothy A. Davis\thanks{ email: DrTimothyAldenDavis@gmail.com, http://www.suitesparse.com. This work was supported by the National Science Foundation, under grants ASC-9111263, DMS-9223088, and CCR-0203270. Portions of the work were done while on sabbatical at Stanford University and Lawrence Berkeley National Laboratory (with funding from Stanford University and the SciDAC program). } \and Iain S. Duff\thanks{Rutherford Appleton Laboratory, Chilton, Didcot, Oxon OX11 0QX, England. email: i.s.duff@rl.ac.uk. http://www.numerical.rl.ac.uk/people/isd/isd.html. This work was supported by the EPSRC under grant GR/R46441. }} \input{amd_version.tex} \maketitle %------------------------------------------------------------------------------ \begin{abstract} AMD is a set of routines that implements the approximate minimum degree ordering algorithm to permute sparse matrices prior to numerical factorization. There are versions written in both C and Fortran 77. A MATLAB interface is included. \end{abstract} %------------------------------------------------------------------------------ AMD Copyright\copyright 1996-2023 by Timothy A. Davis, Patrick R. Amestoy, and Iain S. Duff. All Rights Reserved. AMD is available under alternate licences; contact T. Davis for details. {\bf AMD License:} Refer to the AMD/Doc/License.txt file for the license for your particular copy of AMD. {\bf Availability:} http://www.suitesparse.com. {\bf Acknowledgments:} This work was supported by the National Science Foundation, under grants ASC-9111263 and DMS-9223088 and CCR-0203270. The conversion to C, the addition of the elimination tree post-ordering, and the handling of dense rows and columns were done while Davis was on sabbatical at Stanford University and Lawrence Berkeley National Laboratory. %------------------------------------------------------------------------------ \newpage \section{Overview} %------------------------------------------------------------------------------ AMD is a set of routines for preordering a sparse matrix prior to numerical factorization. It uses an approximate minimum degree ordering algorithm \cite{AmestoyDavisDuff96,AmestoyDavisDuff04} to find a permutation matrix $\m{P}$ so that the Cholesky factorization $\m{PAP}\tr=\m{LL}\tr$ has fewer (often much fewer) nonzero entries than the Cholesky factorization of $\m{A}$. The algorithm is typically much faster than other ordering methods and minimum degree ordering algorithms that compute an exact degree \cite{GeorgeLiu89}. Some methods, such as approximate deficiency \cite{RothbergEisenstat98} and graph-partitioning based methods \cite{Chaco,KarypisKumar98e,PellegriniRomanAmestoy00,schu:01} can produce better orderings, depending on the matrix. The algorithm starts with an undirected graph representation of a symmetric sparse matrix $\m{A}$. Node $i$ in the graph corresponds to row and column $i$ of the matrix, and there is an edge $(i,j)$ in the graph if $a_{ij}$ is nonzero. The degree of a node is initialized to the number of off-diagonal nonzeros in row $i$, which is the size of the set of nodes adjacent to $i$ in the graph. The selection of a pivot $a_{ii}$ from the diagonal of $\m{A}$ and the first step of Gaussian elimination corresponds to one step of graph elimination. Numerical fill-in causes new nonzero entries in the matrix (fill-in refers to nonzeros in $\m{L}$ that are not in $\m{A}$). Node $i$ is eliminated and edges are added to its neighbors so that they form a clique (or {\em element}). To reduce fill-in, node $i$ is selected as the node of least degree in the graph. This process repeats until the graph is eliminated. The clique is represented implicitly. Rather than listing all the new edges in the graph, a single list of nodes is kept which represents the clique. This list corresponds to the nonzero pattern of the first column of $\m{L}$. As the elimination proceeds, some of these cliques become subsets of subsequent cliques, and are removed. This graph can be stored in place, that is using the same amount of memory as the original graph. The most costly part of the minimum degree algorithm is the recomputation of the degrees of nodes adjacent to the current pivot element. Rather than keep track of the exact degree, the approximate minimum degree algorithm finds an upper bound on the degree that is easier to compute. For nodes of least degree, this bound tends to be tight. Using the approximate degree instead of the exact degree leads to a substantial savings in run time, particularly for very irregularly structured matrices. It has no effect on the quality of the ordering. In the C version of AMD, the elimination phase is followed by an elimination tree post-ordering. This has no effect on fill-in, but reorganizes the ordering so that the subsequent numerical factorization is more efficient. It also includes a pre-processing phase in which nodes of very high degree are removed (without causing fill-in), and placed last in the permutation $\m{P}$. This reduces the run time substantially if the matrix has a few rows with many nonzero entries, and has little effect on the quality of the ordering. The C version operates on the symmetric nonzero pattern of $\m{A}+\m{A}\tr$, so it can be given an unsymmetric matrix, or either the lower or upper triangular part of a symmetric matrix. The two Fortran versions of AMD are essentially identical to two versions of the AMD algorithm discussed in an earlier paper \cite{AmestoyDavisDuff96} (approximate minimum external degree, both with and without aggressive absorption). For a discussion of the long history of the minimum degree algorithm, see \cite{GeorgeLiu89}. %------------------------------------------------------------------------------ \section{Availability} %------------------------------------------------------------------------------ In addition to appearing as a Collected Algorithm of the ACM, \newline AMD is available at http://www.suitesparse.com. The Fortran version is available as the routine {\tt MC47} in HSL (formerly the Harwell Subroutine Library) \cite{hsl:2002}. %------------------------------------------------------------------------------ \section{Using AMD in MATLAB} %------------------------------------------------------------------------------ The MATLAB function {\tt amd} is now a built-in function in MATLAB 7.3 (R2006b). The built-in {\tt amd} and the {\tt amd2} function provided here differ in how the optional parameters are passed (the 2nd input parameter). To use AMD2 in MATLAB, you must first compile the AMD2 mexFunction. Just type {\tt make} in the Unix system shell, while in the {\tt AMD/MATLAB} directory. You can also type {\tt amd\_make} in MATLAB, while in the {\tt AMD/MATLAB} directory. Place the {\tt AMD/MATLAB} directory in your MATLAB path. This can be done on any system with MATLAB, including Windows. See Section~\ref{Install} for more details on how to install AMD. The MATLAB statement {\tt p=amd(A)} finds a permutation vector {\tt p} such that the Cholesky factorization {\tt chol(A(p,p))} is typically sparser than {\tt chol(A)}. If {\tt A} is unsymmetric, {\tt amd(A)} is identical to {\tt amd(A+A')} (ignoring numerical cancellation). If {\tt A} is not symmetric positive definite, but has substantial diagonal entries and a mostly symmetric nonzero pattern, then this ordering is also suitable for LU factorization. A partial pivoting threshold may be required to prevent pivots from being selected off the diagonal, such as the statement {\tt [L,U,P] = lu (A (p,p), 0.1)}. Type {\tt help lu} for more details. The statement {\tt [L,U,P,Q] = lu (A (p,p))} in MATLAB 6.5 is not suitable, however, because it uses UMFPACK Version 4.0 and thus does not attempt to select pivots from the diagonal. UMFPACK Version 4.1 in MATLAB 7.0 and later uses several strategies, including a symmetric pivoting strategy, and will give you better results if you want to factorize an unsymmetric matrix of this type. Refer to the UMFPACK User Guide for more details, at http://www.suitesparse.com. The AMD mexFunction is much faster than the built-in MATLAB symmetric minimum degree ordering methods, SYMAMD and SYMMMD. Its ordering quality is comparable to SYMAMD, and better than SYMMMD \cite{DavisGilbertLarimoreNg04}. An optional input argument can be used to modify the control parameters for AMD (aggressive absorption, dense row/column handling, and printing of statistics). An optional output argument provides statistics on the ordering, including an analysis of the fill-in and the floating-point operation count for a subsequent factorization. For more details (once AMD is installed), type {\tt help amd} in the MATLAB command window. %------------------------------------------------------------------------------ \section{Using AMD in a C program} \label{Cversion} %------------------------------------------------------------------------------ The C-callable AMD library consists of eight user-callable routines and one include file. There are two versions of seven of the routines, with \verb'int32_t' and \verb'int64_t' integers. The routines with prefix {\tt amd\_l\_} use \verb'int64_t' integer arguments; the others use \verb'int32_t' integer arguments. The following routines are fully described in Section~\ref{Primary}: \begin{itemize} \item {\tt amd\_order} (\verb'int64_t' version: {\tt amd\_l\_order}) {\footnotesize \begin{verbatim} #include "amd.h" int32_t n, Ap [n+1], Ai [nz], P [n] ; double Control [AMD_CONTROL], Info [AMD_INFO] ; int result = amd_order (n, Ap, Ai, P, Control, Info) ; \end{verbatim} } Computes the approximate minimum degree ordering of an $n$-by-$n$ matrix $\m{A}$. Returns a permutation vector {\tt P} of size {\tt n}, where {\tt P[k] = i} if row and column {\tt i} are the {\tt k}th row and column in the permuted matrix. This routine allocates its own memory of size $1.2e+9n$ integers, where $e$ is the number of nonzeros in $\m{A}+\m{A}\tr$. It computes statistics about the matrix $\m{A}$, such as the symmetry of its nonzero pattern, the number of nonzeros in $\m{L}$, and the number of floating-point operations required for Cholesky and LU factorizations (which are returned in the {\tt Info} array). The user's input matrix is not modified. It returns {\tt AMD\_OK} if successful, {\tt AMD\_OK\_BUT\_JUMBLED} if successful (but the matrix had unsorted and/or duplicate row indices), {\tt AMD\_INVALID} if the matrix is invalid, {\tt AMD\_OUT\_OF\_MEMORY} if out of memory. \item {\tt amd\_defaults} (\verb'int64_t' version: {\tt amd\_l\_defaults}) {\footnotesize \begin{verbatim} #include "amd.h" double Control [AMD_CONTROL] ; amd_defaults (Control) ; \end{verbatim} } Sets the default control parameters in the {\tt Control} array. These can then be modified as desired before passing the array to the other AMD routines. \item {\tt amd\_control} (\verb'int64_t' version: {\tt amd\_l\_control}) {\footnotesize \begin{verbatim} #include "amd.h" double Control [AMD_CONTROL] ; amd_control (Control) ; \end{verbatim} } Prints a description of the control parameters, and their values. \item {\tt amd\_info} (\verb'int64_t' version: {\tt amd\_l\_info}) {\footnotesize \begin{verbatim} #include "amd.h" double Info [AMD_INFO] ; amd_info (Info) ; \end{verbatim} } Prints a description of the statistics computed by AMD, and their values. \item {\tt amd\_valid} (\verb'int64_t' version: {\tt amd\_l\_valid}) {\footnotesize \begin{verbatim} #include "amd.h" int32_t n, Ap [n+1], Ai [nz] ; int result = amd_valid (n, n, Ap, Ai) ; \end{verbatim} } Returns {\tt AMD\_OK} or {\tt AMD\_OK\_BUT\_JUMBLED} if the matrix is valid as input to {\tt amd\_order}; the latter is returned if the matrix has unsorted and/or duplicate row indices in one or more columns. Returns {\tt AMD\_INVALID} if the matrix cannot be passed to {\tt amd\_order}. For {\tt amd\_order}, the matrix must also be square. The first two arguments are the number of rows and the number of columns of the matrix. For its use in AMD, these must both equal {\tt n}. \item {\tt amd\_2} (\verb'int64_t' version: {\tt amd\_l2}) AMD ordering kernel. It is faster than {\tt amd\_order}, and can be called by the user, but it is difficult to use. It does not check its inputs for errors. It does not require the columns of its input matrix to be sorted, but it destroys the matrix on output. Additional workspace must be passed. Refer to the source file {\tt AMD/Source/amd\_2.c} for a description. \item \verb'amd_version': returns the AMD version. \end{itemize} The nonzero pattern of the matrix $\m{A}$ is represented in compressed column form. For an $n$-by-$n$ matrix $\m{A}$ with {\tt nz} nonzero entries, the representation consists of two arrays: {\tt Ap} of size {\tt n+1} and {\tt Ai} of size {\tt nz}. The row indices of entries in column {\tt j} are stored in {\tt Ai[Ap[j]} $\ldots$ {\tt Ap[j+1]-1]}. For {\tt amd\_order}, if duplicate row indices are present, or if the row indices in any given column are not sorted in ascending order, then {\tt amd\_order} creates an internal copy of the matrix with sorted rows and no duplicate entries, and orders the copy. This adds slightly to the time and memory usage of {\tt amd\_order}, but is not an error condition. The matrix is 0-based, and thus row indices must be in the range {\tt 0} to {\tt n-1}. The first entry {\tt Ap[0]} must be zero. The total number of entries in the matrix is thus {\tt nz = Ap[n]}. The matrix must be square, but it does not need to be symmetric. The {\tt amd\_order} routine constructs the nonzero pattern of $\m{B} = \m{A}+\m{A}\tr$ (without forming $\m{A}\tr$ explicitly if $\m{A}$ has sorted columns and no duplicate entries), and then orders the matrix $\m{B}$. Thus, either the lower triangular part of $\m{A}$, the upper triangular part, or any combination may be passed. The transpose $\m{A}\tr$ may also be passed to {\tt amd\_order}. The diagonal entries may be present, but are ignored. %------------------------------------------------------------------------------ \subsection{Control parameters} \label{control_param} %------------------------------------------------------------------------------ Control parameters are set in an optional {\tt Control} array. It is optional in the sense that if a {\tt NULL} pointer is passed for the {\tt Control} input argument, then default control parameters are used. % \begin{itemize} \item {\tt Control[AMD\_DENSE]} (or {\tt Control(1)} in MATLAB): controls the threshold for ``dense'' rows/columns. A dense row/column in $\m{A}+\m{A}\tr$ can cause AMD to spend significant time in ordering the matrix. If {\tt Control[AMD\_DENSE]} $\ge 0$, rows/columns with more than {\tt Control[AMD\_DENSE]} $\sqrt{n}$ entries are ignored during the ordering, and placed last in the output order. The default value of {\tt Control[AMD\_DENSE]} is 10. If negative, no rows/columns are treated as ``dense.'' Rows/columns with 16 or fewer off-diagonal entries are never considered ``dense.'' % \item {\tt Control[AMD\_AGGRESSIVE]} (or {\tt Control(2)} in MATLAB): controls whether or not to use aggressive absorption, in which a prior element is absorbed into the current element if it is a subset of the current element, even if it is not adjacent to the current pivot element (refer to \cite{AmestoyDavisDuff96,AmestoyDavisDuff04} for more details). The default value is nonzero, which means that aggressive absorption will be performed. This nearly always leads to a better ordering (because the approximate degrees are more accurate) and a lower execution time. There are cases where it can lead to a slightly worse ordering, however. To turn it off, set {\tt Control[AMD\_AGGRESSIVE]} to 0. % \end{itemize} Statistics are returned in the {\tt Info} array (if {\tt Info} is {\tt NULL}, then no statistics are returned). Refer to {\tt amd.h} file, for more details (14 different statistics are returned, so the list is not included here). %------------------------------------------------------------------------------ \subsection{Sample C program} %------------------------------------------------------------------------------ The following program, {\tt amd\_demo.c}, illustrates the basic use of AMD. See Section~\ref{Synopsis} for a short description of each calling sequence. {\footnotesize \begin{verbatim} #include "amd.h" int32_t n = 5 ; int32_t Ap [ ] = { 0, 2, 6, 10, 12, 14} ; int32_t Ai [ ] = { 0,1, 0,1,2,4, 1,2,3,4, 2,3, 1,4 } ; int32_t P [5] ; int main (void) { int32_t k ; (void) amd_order (n, Ap, Ai, P, (double *) NULL, (double *) NULL) ; for (k = 0 ; k < n ; k++) printf ("P [%d] = %d\n", k, P [k]) ; return (0) ; } \end{verbatim} } The {\tt Ap} and {\tt Ai} arrays represent the binary matrix \[ \m{A} = \left[ \begin{array}{rrrrr} 1 & 1 & 0 & 0 & 0 \\ 1 & 1 & 1 & 0 & 1 \\ 0 & 1 & 1 & 1 & 0 \\ 0 & 0 & 1 & 1 & 0 \\ 0 & 1 & 1 & 0 & 1 \\ \end{array} \right]. \] The diagonal entries are ignored. % AMD constructs the pattern of $\m{A}+\m{A}\tr$, and returns a permutation vector of $(0, 3, 1, 4, 2)$. % Since the matrix is unsymmetric but with a mostly symmetric nonzero pattern, this would be a suitable permutation for an LU factorization of a matrix with this nonzero pattern and whose diagonal entries are not too small. The program uses default control settings and does not return any statistics about the ordering, factorization, or solution ({\tt Control} and {\tt Info} are both {\tt (double *) NULL}). It also ignores the status value returned by {\tt amd\_order}. More example programs are included with the AMD package. The {\tt amd\_demo.c} program provides a more detailed demo of AMD. Another example is the AMD mexFunction, {\tt amd\_mex.c}. %------------------------------------------------------------------------------ \subsection{A note about zero-sized arrays} %------------------------------------------------------------------------------ AMD uses several user-provided arrays of size {\tt n} or {\tt nz}. Either {\tt n} or {\tt nz} can be zero. If you attempt to {\tt malloc} an array of size zero, however, {\tt malloc} will return a null pointer which AMD will report as invalid. If you {\tt malloc} an array of size {\tt n} or {\tt nz} to pass to AMD, make sure that you handle the {\tt n} = 0 and {\tt nz = 0} cases correctly. %------------------------------------------------------------------------------ \section{Synopsis of C-callable routines} \label{Synopsis} %------------------------------------------------------------------------------ The matrix $\m{A}$ is {\tt n}-by-{\tt n} with {\tt nz} entries. {\footnotesize \begin{verbatim} #include "amd.h" int32_t n, status, Ap [n+1], Ai [nz], P [n] ; double Control [AMD_CONTROL], Info [AMD_INFO] ; amd_defaults (Control) ; status = amd_order (n, Ap, Ai, P, Control, Info) ; amd_control (Control) ; amd_info (Info) ; status = amd_valid (n, n, Ap, Ai) ; \end{verbatim} } The {\tt amd\_l\_*} routines are identical, except that all \verb'int32_t' arguments become \verb'int64_t': {\footnotesize \begin{verbatim} #include "amd.h" int64_t n, status, Ap [n+1], Ai [nz], P [n] ; double Control [AMD_CONTROL], Info [AMD_INFO] ; amd_l_defaults (Control) ; status = amd_l_order (n, Ap, Ai, P, Control, Info) ; amd_l_control (Control) ; amd_l_info (Info) ; status = amd_l_valid (n, n, Ap, Ai) ; \end{verbatim} } The \verb'amd_version' function uses plain \verb'int': {\footnotesize \begin{verbatim} #include "amd.h" int version [3] ; amd_version (version) ; \end{verbatim} } %------------------------------------------------------------------------------ \section{Using AMD in a Fortran program} %------------------------------------------------------------------------------ Two Fortran versions of AMD are provided. The {\tt AMD} routine computes the approximate minimum degree ordering, using aggressive absorption. The {\tt AMDBAR} routine is identical, except that it does not perform aggressive absorption. The {\tt AMD} routine is essentially identical to the HSL routine {\tt MC47B/BD}. Note that earlier versions of the Fortran {\tt AMD} and {\tt AMDBAR} routines included an {\tt IOVFLO} argument, which is no longer present. In contrast to the C version, the Fortran routines require a symmetric nonzero pattern, with no diagonal entries present although the {\tt MC47A/AD} wrapper in HSL allows duplicates, ignores out-of-range entries, and only uses entries from the upper triangular part of the matrix. Although we have an experimental Fortran code for treating ``dense'' rows, the Fortran codes in this release do not treat ``dense'' rows and columns of $\m{A}$ differently, and thus their run time can be high if there are a few dense rows and columns in the matrix. They do not perform a post-ordering of the elimination tree, compute statistics on the ordering, or check the validity of their input arguments. These facilities are provided by {\tt MC47A/AD} and other subroutines from HSL. Only one {\tt integer} version of each Fortran routine is provided. Both Fortran routines overwrite the user's input matrix, in contrast to the C version. % The C version does not return the elimination or assembly tree. The Fortran version returns an assembly tree; refer to the User Guide for details. The following is the syntax of the {\tt AMD} Fortran routine. The {\tt AMDBAR} routine is identical except for the routine name. {\footnotesize \begin{verbatim} INTEGER N, IWLEN, PFREE, NCMPA, IW (IWLEN), PE (N), DEGREE (N), NV (N), $ NEXT (N), LAST (N), HEAD (N), ELEN (N), W (N), LEN (N) CALL AMD (N, PE, IW, LEN, IWLEN, PFREE, NV, NEXT, $ LAST, HEAD, ELEN, DEGREE, NCMPA, W) CALL AMDBAR (N, PE, IW, LEN, IWLEN, PFREE, NV, NEXT, $ LAST, HEAD, ELEN, DEGREE, NCMPA, W) \end{verbatim} } The input matrix is provided to {\tt AMD} and {\tt AMDBAR} in three arrays, {\tt PE}, of size {\tt N}, {\tt LEN}, of size {\tt N}, and {\tt IW}, of size {\tt IWLEN}. The size of {\tt IW} must be at least {\tt NZ+N}. The recommended size is {\tt 1.2*NZ + N}. On input, the indices of nonzero entries in row {\tt I} are stored in {\tt IW}. {\tt PE(I)} is the index in {\tt IW} of the start of row {\tt I}. {\tt LEN(I)} is the number of entries in row {\tt I}. The matrix is 1-based, with row and column indices in the range 1 to {\tt N}. Row {\tt I} is contained in {\tt IW (PE(I)} $\ldots \:$ {\tt PE(I) + LEN(I) - 1)}. The diagonal entries must not be present. The indices within each row must not contain any duplicates, but they need not be sorted. The rows themselves need not be in any particular order, and there may be empty space between the rows. If {\tt LEN(I)} is zero, then there are no off-diagonal entries in row {\tt I}, and {\tt PE(I)} is ignored. The integer {\tt PFREE} defines what part of {\tt IW} contains the user's input matrix, which is held in {\tt IW(1}~$\ldots~\:${\tt PFREE-1)}. The contents of {\tt IW} and {\tt LEN} are undefined on output, and {\tt PE} is modified to contain information about the ordering. As the algorithm proceeds, it modifies the {\tt IW} array, placing the pattern of the partially eliminated matrix in {\tt IW(PFREE} $\ldots \:${\tt IWLEN)}. If this space is exhausted, the space is compressed. The number of compressions performed on the {\tt IW} array is returned in the scalar {\tt NCMPA}. The value of {\tt PFREE} on output is the length of {\tt IW} required for no compressions to be needed. The output permutation is returned in the array {\tt LAST}, of size {\tt N}. If {\tt I=LAST(K)}, then {\tt I} is the {\tt K}th row in the permuted matrix. The inverse permutation is returned in the array {\tt ELEN}, where {\tt K=ELEN(I)} if {\tt I} is the {\tt K}th row in the permuted matrix. On output, the {\tt PE} and {\tt NV} arrays hold the assembly tree, a supernodal elimination tree that represents the relationship between columns of the Cholesky factor $\m{L}$. If {\tt NV(I)} $> 0$, then {\tt I} is a node in the assembly tree, and the parent of {\tt I} is {\tt -PE(I)}. If {\tt I} is a root of the tree, then {\tt PE(I)} is zero. The value of {\tt NV(I)} is the number of entries in the corresponding column of $\m{L}$, including the diagonal. If {\tt NV(I)} is zero, then {\tt I} is a non-principal node that is not in the assembly tree. Node {\tt -PE(I)} is the parent of node {\tt I} in a subtree, the root of which is a node in the assembly tree. All nodes in one subtree belong to the same supernode in the assembly tree. The other size {\tt N} arrays ({\tt DEGREE}, {\tt HEAD}, {\tt NEXT}, and {\tt W}) are used as workspace, and are not defined on input or output. If you want to use a simpler user-interface and compute the elimination tree post-ordering, you should be able to call the C routines {\tt amd\_order} or {\tt amd\_l\_order} from a Fortran program. Just be sure to take into account the 0-based indexing in the {\tt P}, {\tt Ap}, and {\tt Ai} arguments to {\tt amd\_order} and {\tt amd\_l\_order}. A sample interface is provided in the files {\tt AMD/Demo/amd\_f77cross.f} and {\tt AMD/Demo/amd\_f77wrapper.c}. To compile the {\tt amd\_f77cross} program, type {\tt make cross} in the {\tt AMD/Demo} directory. The Fortran-to-C calling conventions are highly non-portable, so this example is not guaranteed to work with your compiler C and Fortran compilers. The output of {\tt amd\_f77cross} is in {\tt amd\_f77cross.out}. %------------------------------------------------------------------------------ \section{Sample Fortran main program} %------------------------------------------------------------------------------ The following program illustrates the basic usage of the Fortran version of AMD. The {\tt AP} and {\tt AI} arrays represent the binary matrix \[ \m{A} = \left[ \begin{array}{rrrrr} 1 & 1 & 0 & 0 & 0 \\ 1 & 1 & 1 & 0 & 1 \\ 0 & 1 & 1 & 1 & 1 \\ 0 & 0 & 1 & 1 & 0 \\ 0 & 1 & 1 & 0 & 1 \\ \end{array} \right] \] in a conventional 1-based column-oriented form, except that the diagonal entries are not present. The matrix has the same as nonzero pattern of $\m{A}+\m{A}\tr$ in the C program, in Section~\ref{Cversion}. The output permutation is $(4, 1, 3, 5, 2)$. It differs from the permutation returned by the C routine {\tt amd\_order} because a post-order of the elimination tree has not yet been performed. {\footnotesize \begin{verbatim} INTEGER N, NZ, J, K, P, IWLEN, PFREE, NCMPA PARAMETER (N = 5, NZ = 10, IWLEN = 17) INTEGER AP (N+1), AI (NZ), LAST (N), PE (N), LEN (N), ELEN (N), $ IW (IWLEN), DEGREE (N), NV (N), NEXT (N), HEAD (N), W (N) DATA AP / 1, 2, 5, 8, 9, 11/ DATA AI / 2, 1,3,5, 2,4,5, 3, 2,3 / C load the matrix into the AMD workspace DO 10 J = 1,N PE (J) = AP (J) LEN (J) = AP (J+1) - AP (J) 10 CONTINUE DO 20 P = 1,NZ IW (P) = AI (P) 20 CONTINUE PFREE = NZ + 1 C order the matrix (destroys the copy of A in IW, PE, and LEN) CALL AMD (N, PE, IW, LEN, IWLEN, PFREE, NV, NEXT, LAST, HEAD, $ ELEN, DEGREE, NCMPA, W) DO 60 K = 1, N PRINT 50, K, LAST (K) 50 FORMAT ('P (',I2,') = ', I2) 60 CONTINUE END \end{verbatim} } The {\tt Demo} directory contains an example of how the C version may be called from a Fortran program, but this is highly non-portable. For this reason, it is placed in the {\tt Demo} directory, not in the primary {\tt Source} directory. %------------------------------------------------------------------------------ \section{Installation} \label{Install} %------------------------------------------------------------------------------ AMD now relies primarily on CMake to build the library. It also includes a simple \verb'AMD/Makefile' which uses cmake to do the actual build. The use of this \verb'AMD/Makefile' is optional; for Windows, just import the CMakeLists.txt into MS Visual Studio. To compile and install the library for both system-wide usage and local usage: \begin{verbatim} make sudo make install \end{verbatim} To compile/install for just local usage (SuiteSparse/lib and SuiteSparse/include): \begin{verbatim} make local make install \end{verbatim} To run the demos \begin{verbatim} make demos \end{verbatim} To remove all files in \verb'AMD/' not in the original distribution (leaves SuiteSparse/lib and SuiteSparse/include unchanged): \begin{verbatim} make clean \end{verbatim} To use the AMD2 mexFunction in MATLAB, simply type {\tt amd\_make} in MATLAB while in the {\tt AMD/MATLAB} directory. This works on any system with MATLAB, including Windows. Alternatively, just use the built-in {\tt amd} in MATLAB 7.3 or later, which is the same as this package. %------------------------------------------------------------------------------ \newpage \section{The AMD routines} \label{Primary} %------------------------------------------------------------------------------ The file {\tt AMD/Include/amd.h} listed below describes each user-callable routine in the C version of AMD, and gives details on their use. {\footnotesize \input{amd_h.tex} } %------------------------------------------------------------------------------ \newpage % References %------------------------------------------------------------------------------ \bibliographystyle{plain} \bibliography{AMD_UserGuide} \end{document}