cholmod

SuiteSparse: A Suite of Sparse matrix packages at http://suitesparse.com

June 29, 2023, SuiteSparse VERSION 7.1.0

SuiteSparse is a set of sparse-matrix-related packages written or co-authored by Tim Davis, available at https://github.com/DrTimothyAldenDavis/SuiteSparse .

Primary author of SuiteSparse (codes and algorithms, excl. METIS): Tim Davis

Code co-authors, in alphabetical order (not including METIS): Patrick Amestoy, David Bateman, Jinhao Chen, Yanqing Chen, Iain Duff, Les Foster, William Hager, Scott Kolodziej, Chris Lourenco, Stefan Larimore, Erick Moreno-Centeno, Ekanathan Palamadai, Sivasankaran Rajamanickam, Sanjay Ranka, Wissam Sid-Lakhdar, Nuri Yeralan.

METIS is authored by George Karypis.

Additional algorithm designers: Esmond Ng and John Gilbert.

Refer to each package for license, copyright, and author information.

SuiteSparse branches

* dev: the default branch, with recent updates of features to appear in
    the next stable release.  The intent is to keep this branch in
    fully working order at all times, but the features will not be
    finalized at any given time.
* stable: the most recent stable release.
* dev2: working branch.  All submitted PRs should made to this branch.
    This branch might not always be in working order.

How to cite the SuiteSparse meta-package and its component packages:

SuiteSparse is a meta-package of many packages, each with their own published papers. To cite the whole collection, use the URLs:

* https://github.com/DrTimothyAldenDavis/SuiteSparse
* http://suitesparse.com (which is a forwarding URL
    to https://people.engr.tamu.edu/davis/suitesparse.html)

Please also cite the specific papers for the packages you use. This is a long list; if you want a shorter list, just cite the most recent "Algorithm XXX:" papers in ACM TOMS, for each package.

* For the MATLAB x=A\b, see below for AMD, COLAMD, CHOLMOD, UMFPACK,
    and SuiteSparseQR (SPQR).

* for GraphBLAS, and `C=A*B` in MATLAB (sparse-times-sparse):

    T. Davis, Algorithm 10xx: SuiteSparse:GraphBLAS: parallel graph
    algorithms in the language of sparse linear algebra, ACM Trans on
    Mathematical Software, to appear, 2023.  See the pdf in
    https://github.com/DrTimothyAldenDavis/GraphBLAS/tree/stable/Doc

    T. Davis, Algorithm 1000: SuiteSparse:GraphBLAS: graph algorithms in
    the language of sparse linear algebra, ACM Trans on Mathematical
    Software, vol 45, no 4, Dec. 2019, Article No 44.
    https://doi.org/10.1145/3322125.

* for CSparse/CXSParse:

    T. A. Davis, Direct Methods for Sparse Linear Systems, SIAM Series on
    the Fundamentals of Algorithms, SIAM, Philadelphia, PA, 2006.
    https://doi.org/10.1137/1.9780898718881

* for SuiteSparseQR (SPQR): (also cite AMD, COLAMD):

    T. A. Davis, Algorithm 915: SuiteSparseQR: Multifrontal multithreaded
    rank-revealing sparse QR factorization, ACM Trans. on Mathematical
    Software, 38(1), 2011, pp. 8:1--8:22.
    https://doi.org/10.1145/2049662.2049670

* for SuiteSparseQR/GPU:

    Sencer Nuri Yeralan, T. A. Davis, Wissam M. Sid-Lakhdar, and Sanjay
    Ranka. 2017. Algorithm 980: Sparse QR Factorization on the GPU.  ACM
    Trans. Math. Softw. 44, 2, Article 17 (June 2018), 29 pages.
    https://doi.org/10.1145/3065870

* for CHOLMOD: (also cite AMD, COLAMD):

    Y. Chen, T. A. Davis, W. W. Hager, and S. Rajamanickam, Algorithm 887:
    CHOLMOD, supernodal sparse Cholesky factorization and update/downdate,
    ACM Trans. on Mathematical Software, 35(3), 2008, pp. 22:1--22:14.
    https://dl.acm.org/doi/abs/10.1145/1391989.1391995

    T. A. Davis and W. W. Hager, Dynamic supernodes in sparse Cholesky
    update/downdate and triangular solves, ACM Trans. on Mathematical
    Software, 35(4), 2009, pp. 27:1--27:23.
    https://doi.org/10.1145/1462173.1462176

* for CHOLMOD/Modify Module: (also cite AMD, COLAMD):

    T. A. Davis and William W. Hager, Row Modifications of a Sparse
    Cholesky Factorization SIAM Journal on Matrix Analysis and Applications
    2005 26:3, 621-639
    https://doi.org/10.1137/S089547980343641X

    T. A. Davis and William W. Hager, Multiple-Rank Modifications of a
    Sparse Cholesky Factorization SIAM Journal on Matrix Analysis and
    Applications 2001 22:4, 997-1013
    https://doi.org/10.1137/S0895479899357346

    T. A. Davis and William W. Hager, Modifying a Sparse Cholesky
    Factorization, SIAM Journal on Matrix Analysis and Applications 1999
    20:3, 606-627
    https://doi.org/10.1137/S0895479897321076

* for CHOLMOD/GPU Modules:

    Steven C. Rennich, Darko Stosic, Timothy A. Davis, Accelerating sparse
    Cholesky factorization on GPUs, Parallel Computing, Vol 59, 2016, pp
    140-150.
    https://doi.org/10.1016/j.parco.2016.06.004

* for AMD and CAMD:

    P. Amestoy, T. A. Davis, and I. S. Duff, Algorithm 837: An approximate
    minimum degree ordering algorithm, ACM Trans. on Mathematical Software,
    30(3), 2004, pp. 381--388.
    https://dl.acm.org/doi/abs/10.1145/1024074.1024081

    P. Amestoy, T. A. Davis, and I. S. Duff, An approximate minimum degree
    ordering algorithm, SIAM J. Matrix Analysis and Applications, 17(4),
    1996, pp. 886--905.
    https://doi.org/10.1137/S0895479894278952

* for COLAMD, SYMAMD, CCOLAMD, and CSYMAMD:

    T. A. Davis, J. R. Gilbert, S. Larimore, E. Ng, Algorithm 836:  COLAMD,
    an approximate column minimum degree ordering algorithm, ACM Trans. on
    Mathematical Software, 30(3), 2004, pp. 377--380.
    https://doi.org/10.1145/1024074.1024080

    T. A. Davis, J. R. Gilbert, S. Larimore, E. Ng, A column approximate
    minimum degree ordering algorithm, ACM Trans. on Mathematical Software,
    30(3), 2004, pp. 353--376.
    https://doi.org/10.1145/1024074.1024079

* for UMFPACK: (also cite AMD and COLAMD):

    T. A. Davis, Algorithm 832:  UMFPACK - an unsymmetric-pattern
    multifrontal method with a column pre-ordering strategy, ACM Trans. on
    Mathematical Software, 30(2), 2004, pp. 196--199.
    https://dl.acm.org/doi/abs/10.1145/992200.992206

    T. A. Davis, A column pre-ordering strategy for the unsymmetric-pattern
    multifrontal method, ACM Trans. on Mathematical Software, 30(2), 2004,
    pp. 165--195.
    https://dl.acm.org/doi/abs/10.1145/992200.992205

    T. A. Davis and I. S. Duff, A combined unifrontal/multifrontal method
    for unsymmetric sparse matrices, ACM Trans. on Mathematical Software,
    25(1), 1999, pp. 1--19.
    https://doi.org/10.1145/305658.287640

    T. A. Davis and I. S. Duff, An unsymmetric-pattern multifrontal method
    for sparse LU factorization, SIAM J. Matrix Analysis and Computations,
    18(1), 1997, pp. 140--158.
    https://doi.org/10.1137/S0895479894246905

* for the FACTORIZE m-file:

    T. A. Davis, Algorithm 930: FACTORIZE, an object-oriented linear system
    solver for MATLAB, ACM Trans. on Mathematical Software, 39(4), 2013,
    pp. 28:1-28:18.
    https://doi.org/10.1145/2491491.2491498

* for KLU and BTF (also cite AMD and COLAMD):

    T. A. Davis and Ekanathan Palamadai Natarajan. 2010. Algorithm 907:
    KLU, A Direct Sparse Solver for Circuit Simulation Problems. ACM Trans.
    Math. Softw. 37, 3, Article 36 (September 2010), 17 pages.
    https://dl.acm.org/doi/abs/10.1145/1824801.1824814

* for LDL:

    T. A. Davis. Algorithm 849: A concise sparse Cholesky factorization
    package. ACM Trans. Math. Softw. 31, 4 (December 2005), 587–591.
    https://doi.org/10.1145/1114268.1114277

* for ssget and the SuiteSparse Matrix Collection:

    T. A. Davis and Yifan Hu. 2011. The University of Florida sparse
    matrix collection. ACM Trans. Math. Softw. 38, 1, Article 1 (November
    2011), 25 pages.
    https://doi.org/10.1145/2049662.2049663

    Kolodziej et al., (2019). The SuiteSparse Matrix Collection Website
    Interface. Journal of Open Source Software, 4(35), 1244,
    https://doi.org/10.21105/joss.01244

* for `spqr_rank`:

    Leslie V. Foster and T. A. Davis. 2013. Algorithm 933: Reliable
    calculation of numerical rank, null space bases, pseudoinverse
    solutions, and basic solutions using suitesparseQR. ACM Trans. Math.
    Softw. 40, 1, Article 7 (September 2013), 23 pages.
    https://doi.org/10.1145/2513109.2513116

* for Mongoose:

    T. A. Davis, William W. Hager, Scott P. Kolodziej, and S. Nuri Yeralan.
    2020. Algorithm 1003: Mongoose, a Graph Coarsening and Partitioning
    Library. ACM Trans. Math. Softw. 46, 1, Article 7 (March 2020), 18
    pages.
    https://doi.org/10.1145/3337792

* for SPEX:

    Christopher Lourenco, Jinhao Chen, Erick Moreno-Centeno, and T. A.
    Davis. 2022. Algorithm 1021: SPEX Left LU, Exactly Solving Sparse
    Linear Systems via a Sparse Left-Looking Integer-Preserving LU
    Factorization. ACM Trans. Math. Softw. June 2022.
    https://doi.org/10.1145/3519024

About the BLAS and LAPACK libraries

NOTE: Use of the Intel MKL BLAS is strongly recommended. In a 2019 test, OpenBLAS caused result in severe performance degradation. The reason for this is being investigated, and this may be resolved in the near future.

To select your BLAS/LAPACK, see the instructions in SuiteSparseBLAS.cmake in

When distributed in a binary form (such as a Debian, Ubuntu, Spack, or Brew package), SuiteSparse should probably be compiled to expect a 32-bit BLAS, since this is the most common case. The default is to use a 32-bit BLAS, but this can be changed in SuiteSparseBLAS.cmake or by compiling with

By default, SuiteSparse hunts for a suitable BLAS library. To enforce a particular BLAS library use either:

CMAKE_OPTIONS="-DBLA_VENDOR=OpenBLAS" make
cd Package ; cmake -DBLA_VENDOR=OpenBLAS .. make

To use the default (hunt for a BLAS), do not set

When selecting a particular BLAS library, the

SuiteSparse/README

Packages in SuiteSparse, and files in this directory:

GraphBLAS   graph algorithms in the language of linear algebra.
            https://graphblas.org
            author: Tim Davis

SPEX        solves sparse linear systems in exact arithmetic.
            Requires the GNU GMP and MPRF libraries.
            This will be soon replaced by a more general package, SPEX v3
            that includes this method (exact sparse LU) and others (sparse
            exact Cholesky, and sparse exact update/downdate).  The API
            of v3 will be changing significantly.

AMD         approximate minimum degree ordering.  This is the built-in AMD
            function in MATLAB.
            authors: Tim Davis, Patrick Amestoy, Iain Duff

bin         where programs are placed when compiled

BTF         permutation to block triangular form
            authors: Tim Davis, Ekanathan Palamadai

CAMD        constrained approximate minimum degree ordering
            authors: Tim Davis, Patrick Amestoy, Iain Duff, Yanqing Chen

CCOLAMD     constrained column approximate minimum degree ordering
            authors: Tim Davis, Sivasankaran Rajamanickam, Stefan Larimore.
                Algorithm design collaborators: Esmond Ng, John Gilbert
                (for COLAMD)

ChangeLog   a summary of changes to SuiteSparse.  See */Doc/ChangeLog
            for details for each package.

CHOLMOD     sparse Cholesky factorization.  Requires AMD, COLAMD, CCOLAMD,
            the BLAS, and LAPACK.  Optionally uses METIS.  This is chol and
            x=A\b in MATLAB.
            author for all modules: Tim Davis
            CHOLMOD/Modify module authors: Tim Davis and William W. Hager

COLAMD      column approximate minimum degree ordering.  This is the
            built-in COLAMD function in MATLAB.
            authors (of the code): Tim Davis and Stefan Larimore
            Algorithm design collaborators: Esmond Ng, John Gilbert

Contents.m  a list of contents for 'help SuiteSparse' in MATLAB.

CSparse     a concise sparse matrix package, developed for my
            book, "Direct Methods for Sparse Linear Systems",
            published by SIAM.  Intended primarily for teaching.
            Note that the code is (c) Tim Davis, as stated in the book.
            For production, use CXSparse instead.  In particular, both
            CSparse and CXSparse have the same include filename: cs.h.
            This package is used for the built-in DMPERM in MATLAB.
            author: Tim Davis

CXSparse    CSparse Extended.  Includes support for complex matrices
            and both int or long integers.  Use this instead of CSparse
            for production use; it creates a libcsparse.so (or *dylib on
            the Mac) with the same name as CSparse.  It is a superset
            of CSparse.  Any code that links against CSparse should
            also be able to link against CXSparse instead.
            author: Tim Davis, David Bateman

include     'make install' places user-visible include files for each
            package here, after 'make local'

KLU         sparse LU factorization, primarily for circuit simulation.
            Requires AMD, COLAMD, and BTF.  Optionally uses CHOLMOD,
            CAMD, CCOLAMD, and METIS.
            authors: Tim Davis, Ekanathan Palamadai

LDL         a very concise LDL' factorization package
            author: Tim Davis

lib         'make install' places shared libraries for each package
            here, after 'make local'

Makefile    to compile all of SuiteSparse

            make            compiles SuiteSparse libraries.
                            Subsequent "make install" will install
                            in just CMAKE_INSTALL_PATH (defaults to
                            /usr/local/lib on Linux or Mac).

            make local      compiles SuiteSparse.
                            Subsequent "make install will install only
                            in ./lib, ./include only.
                            Does not install in CMAKE_INSTALL_PATH.

            make global     compiles SuiteSparse libraries.
                            Subsequent "make install" will install in
                            just /usr/local/lib (or whatever your
                            CMAKE_INSTALL_PREFIX is).
                            Does not install in ./lib and ./include.

            make install    installs in the current directory
                            (./lib, ./include), and/or in
                            /usr/local/lib and /usr/local/include,
                            depending on whether "make", "make local",
                            or "make global" has been done.

            make uninstall  undoes 'make install'

            make distclean  removes all files not in distribution, including
                            ./bin, ./share, ./lib, and ./include.

            make purge      same as 'make distclean'

            make clean      removes all files not in distribution, but
                            keeps compiled libraries and demoes, ./lib,
                            ./share, and ./include.

            Each individual package also has each of the above 'make'
            targets.

            Things you don't need to do:
            make docs       creates user guides from LaTeX files
            make cov        runs statement coverage tests (Linux only)

MATLAB_Tools    various m-files for use in MATLAB
            author: Tim Davis (all parts)
            for spqr_rank: author Les Foster and Tim Davis

            Contents.m      list of contents
            dimacs10        loads matrices for DIMACS10 collection
            Factorize       object-oriented x=A\b for MATLAB
            find_components finds connected components in an image
            GEE             simple Gaussian elimination
            getversion.m    determine MATLAB version
            gipper.m        create MATLAB archive
            hprintf.m       print hyperlinks in command window
            LINFACTOR       predecessor to Factorize package
            MESHND          nested dissection ordering of regular meshes
            pagerankdemo.m  illustrates how PageRank works
            SFMULT          C=S*F where S is sparse and F is full
            shellgui        display a seashell
            sparseinv       sparse inverse subset
            spok            check if a sparse matrix is valid
            spqr_rank       SPQR_RANK package.  MATLAB toolbox for rank
                            deficient sparse matrices: null spaces,
                            reliable factorizations, etc.  With Leslie
                            Foster, San Jose State Univ.
            SSMULT          C=A*B where A and B are both sparse
            SuiteSparseCollection    for the SuiteSparse Matrix Collection
            waitmex         waitbar for use inside a mexFunction

            The SSMULT and SFMULT functions are the basis for the
            built-in C=A*B functions in MATLAB.

Mongoose    graph partitioning.
            authors: Nuri Yeralan, Scott Kolodziej, William Hager, Tim Davis

CHOLMOD/SuiteSparse_metis: a modified version of METIS, embedded into
            the CHOLMOD library.  See the README.txt files
            for details.  author: George Karypis.  This is a slightly
            modified copy included with SuiteSparse via the open-source
            license provided by George Karypis.  SuiteSparse cannot use
            an unmodified copy METIS.

RBio        read/write sparse matrices in Rutherford/Boeing format
            author: Tim Davis

README.txt  this file

SPQR        sparse QR factorization.  This the built-in qr and x=A\b in
            MATLAB.  Also called SuiteSparseQR.
            author of the CPU code: Tim Davis
            author of GPU modules: Tim Davis, Nuri Yeralan,
                Wissam Sid-Lakhdar, Sanjay Ranka

GPUQREngine: GPU support package for SPQR
            (not built into MATLAB, however)
            authors: Tim Davis, Nuri Yeralan, Sanjay Ranka,
                Wissam Sid-Lakhdar

SuiteSparse_config    configuration file for all the above packages.
            CSparse and MATLAB_Tools do not use SuiteSparse_config.
            author: Tim Davis

SuiteSparse_GPURuntime      GPU support package for SPQR and CHOLMOD
            (not builtin to MATLAB, however).

SuiteSparse_install.m       install SuiteSparse for MATLAB
SuiteSparse_paths.m         set paths for SuiteSparse MATLAB mexFunctions

SuiteSparse_test.m          exhaustive test for SuiteSparse in MATLAB

ssget       MATLAB interface to the SuiteSparse Matrix Collection
            author: Tim Davis

UMFPACK     sparse LU factorization.  Requires AMD and the BLAS.
            This is the built-in lu and x=A\b in MATLAB.
            author: Tim Davis
            algorithm design collaboration: Iain Duff

Some codes optionally use METIS 5.1.0. This package is located in SuiteSparse in the

Refer to each package for license, copyright, and author information. All codes are authored or co-authored by Timothy A. Davis (email: davis@tamu.edu), except for METIS (by George Karypis), GraphBLAS/cpu_features (by Google), GraphBLAS/lz4 and zstd (by Yann Collet, now at Facebook), and GraphBLAS/CUDA/jitify.hpp (by NVIDIA). Parts of GraphBLAS/CUDA are Copyright (c) by NVIDIA. Please refer to each of these licenses.

Licenses for each package are located in the following files, all in PACKAGENAME/Doc/License.txt, and these files are also concatenated into the top-level LICENSE.txt file.

QUICK START FOR MATLAB USERS (Linux or Mac):

Uncompress the SuiteSparse.zip or SuiteSparse.tar.gz archive file (they contain the same thing). Suppose you place SuiteSparse in the /home/me/SuiteSparse folder.

Add the SuiteSparse/lib folder to your run-time library path. On Linux, add this to your ~/.bashrc script, assuming /home/me/SuiteSparse is the location of your copy of SuiteSparse:

LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/me/SuiteSparse/lib
export LD_LIBRARY_PATH

For the Mac, use this instead, in your ~/.zshrc script, assuming you place SuiteSparse in /Users/me/SuiteSparse:

DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/Users/me/SuiteSparse/lib
export DYLD_LIBRARY_PATH

Compile all of SuiteSparse with "make local".

Next, compile the GraphBLAS MATLAB library. In the system shell while in the SuiteSparse folder, type "make gbmatlab" if you want to install it system-wide with "make install", or "make gblocal" if you want to use the library in your own SuiteSparse/lib.

Then in the MATLAB Command Window, cd to the SuiteSparse directory and type

Save your MATLAB path for future sessions with the MATLAB pathtool or savepath commands. If those methods fail because you don't have system-wide permission, add the new paths to your startup.m file, normally in Documents/MATLAB/startup.m. You can also use the

QUICK START FOR THE C/C++ LIBRARIES:

For Linux and Mac: type the following in this directory (requires system priviledge to do the

make
sudo make install

All libraries will be created and copied into SuiteSparse/lib and into /usr/local/lib. All include files need by the applications that use SuiteSparse are copied into SuiteSparse/include and into /usr/local/include.

For Windows, import each

cd SuiteSparse_config/build
cmake ..
cmke --build . --config Release

Be sure to first install all required libraries: BLAS and LAPACK for UMFPACK, CHOLMOD, and SPQR, and GMP and MPFR for SPEX. Be sure to use the latest libraries; SPEX requires MPFR 4.0.2 and GMP 6.1.2 (these version numbers do NOT correspond to the X.Y.Z suffix of libgmp.so.X.Y.Z and libmpfr.so.X.Y.Z; see the SPEX user guide for details).

To compile the libraries and install them only in SuiteSparse/lib (not /usr/local/lib), do this instead in the top-level of SuiteSparse:

make local

If you add /home/me/SuiteSparse/lib to your library search path (

S = /home/me/SuiteSparse
cc myprogram.c -I$(S)/include -lumfpack -lamd -lcholmod -lsuitesparseconfig -lm

To change the C and C++ compilers, and to compile in parallel use:

CC=gcc CX=g++ JOBS=32 make

for example, which changes the compiler to gcc and g++, and runs make with 'make -j32', in parallel with 32 jobs.

This will work on Linux/Unix and the Mac. It should automatically detect if you have the Intel compilers or not, and whether or not you have CUDA.

NOTE: Use of the Intel MKL BLAS is strongly recommended. The OpenBLAS can (rarely) result in severe performance degradation, in CHOLMOD in particular. The reason for this is still under investigation and might already be resolved in the current version of OpenBLAS. See

You may also need to add SuiteSparse/lib to your path. If your copy of SuiteSparse is in /home/me/SuiteSparse, for example, then add this to your ~/.bashrc file:

LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/me/SuiteSparse/lib
export LD_LIBRARY_PATH

For the Mac, use this instead:

DYLD_LIBRARY_PATH=$DYLD_LIBRARY_PATH:/home/me/SuiteSparse/lib
export DYLD_LIBRARY_PATH

Python and Rust interfaces

See scikit-sparse and scikit-umfpack for the Python interface via SciPy:

https://github.com/scikit-sparse/scikit-sparse

https://github.com/scikit-umfpack/scikit-umfpack

See russell for a Rust interface:

https://github.com/cpmech/russell

Compilation options

You can set specific options for CMake with the command (for example):

CMAKE_OPTIONS="-DNPARTITION=1 -DNSTATIC=1 -DCMAKE_BUILD_TYPE=Debug" make

That command will compile all of SuiteSparse except for CHOLMOD/Partition Module. Debug mode will be used. The static libraries will not be built (NSTATIC is true).

CMAKE_BUILD_TYPE:   Default: "Release", use "Debug" for debugging.

ENABLE_CUDA:        if set to true, CUDA is enabled for the project.
                    Default: true for CHOLMOD and SPQR; false otherwise

LOCAL_INSTALL:      if true, "cmake --install" will install
                    into SuiteSparse/lib and SuiteSparse/include.
                    if false, "cmake --install" will install into the
                    default prefix (or the one configured with
                    CMAKE_INSTALL_PREFIX).
                    Default: false

NSTATIC:            if true, static libraries are not built.
                    Default: false, except for GraphBLAS, which
                    takes a long time to compile so the default for
                    GraphBLAS is true.  For Mongoose, the NSTATIC setting
                    is treated as if it always false, since the mongoose
                    program is built with the static library.

SUITESPARSE_CUDA_ARCHITECTURES:  a string, such as "all" or
                    "35;50;75;80" that lists the CUDA architectures to use
                    when compiling CUDA kernels with nvcc.  The "all"
                    option requires cmake 3.23 or later.
                    Default: "52;75;80".

BLA_VENDOR          a string.  Leave unset, or use "ANY" to select any BLAS
                    library (the default).  Or set to the name of a
                    BLA_VENDOR defined by FindBLAS.cmake.  See:
                    https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors

ALLOW_64BIT_BLAS    if true: look for a 64-bit BLAS.  If false: 32-bit only.
                    Default: false.

NOPENMP             if true: OpenMP is not used.  Default: false.
                    UMFPACK, CHOLMOD, SPQR, and GraphBLAS will be slow.
                    Note that BLAS and LAPACK may still use OpenMP
                    internally; if you wish to disable OpenMP in an entire
                    application, select a single-threaded BLAS/LAPACK.
                    WARNING: GraphBLAS may not be thread-safe if built
                    without OpenMP (see the User Guide for details).

DEMO                if true: build the demo programs for each package.
                    Default: false.

Additional options are available within specific packages:

NCHOLMOD            if true, UMFPACK and KLU do not use CHOLMOD for
                    additional (optional) ordering options

CHOLMOD is composed of a set of Modules that can be independently selected; all options default to false:

NGL                 if true: do not build any GPL-licensed module
                    (MatrixOps, Modify, Supernodal, and GPU modules)
NCHECK              if true: do not build the Check module.
NMATRIXOPS          if true: do not build the MatrixOps module.
NCHOLESKY           if true: do not build the Cholesky module.
                    This also disables the Supernodal and Modify modules.
NMODIFY             if true: do not build the Modify module.
NCAMD               if true: do not link against CAMD and CCOLAMD.
                    This also disables the Partition module.
NPARTITION          if true: do not build the Partition module.
NSUPERNODAL         if true: do not build the Supernodal module.

Acknowledgements

I would like to thank François Bissey, Sebastien Villemot, Erik Welch, Jim Kitchen, Markus Mützel, and Fabian Wein for their valuable feedback on the SuiteSparse build system and how it works with various Linux / Python distros and other package managers. If you are a maintainer of a SuiteSparse packaging for a Linux distro, conda-forge, R, spack, brew, vcpkg, etc, please feel free to contact me if there's anything I can do to make your life easier.

See also the various Acknowledgements within each package.