SuiteSparse: A Suite of Sparse matrix packages at http://suitesparse.com
Oct 31, 2023, SuiteSparse VERSION 7.3.1
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.
* 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.
Thanks for packaging SuiteSparse! Here are some suggestions:
* GraphBLAS takes a long time to compile because it creates many fast
"FactoryKernels" at compile-time. If you want to reduce the compile
time and library size, enable the COMPACT mode, but keep the JIT
enabled. Then GraphBLAS will compile the kernels it needs at run-time,
via its JIT. Performance will be the same as the FactoryKernels once
the JIT kernels are compiled. User compiled kernels are placed in
~/.SuiteSparse, by default. You do not need to distribute the source
for GraphBLAS to enable the JIT: just libgraphblas.so and GraphBLAS.h
is enough.
* GraphBLAS needs OpenMP! It's fundamentally a parallel code so please
distribute it with OpenMP enabled. Performance will suffer
otherwise.
* CUDA acceleration: CHOLMOD and SPQR can benefit from their CUDA
kernels. If you do not have CUDA or do not want to include it in
your distro, this version of SuiteSparse skips the building of
the CHOLMOD_CUDA and SPQR_CUDA libraries, and does not link
against the GPUQREngine and SuiteSparse_GPURuntime libraries.
The latter can be excluded from your distro (the "make" command
will build them, but they will be empty).
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. A. Davis. Algorithm 1037: SuiteSparse:GraphBLAS: Parallel Graph
Algorithms in the Language of Sparse Linear Algebra. ACM Trans. Math.
Softw. 49, 3, Article 28 (September 2023), 30 pages.
https://doi.org/10.1145/3577195
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
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
SuiteSparse_config/cmake_modules. If SuiteSparse_config finds a BLAS with
64-bit integers (such as the Intel MKL ilp64 BLAS), it configures
SuiteSparse_config.h with the SUITESPARSE_BLAS_INT defined as int64_t.
Otherwise, if a 32-bit BLAS is found, this type is defined as int32_t. If
later on, UMFPACK, CHOLMOD, or SPQR are compiled and linked with a BLAS that
has a different integer size, you must override the definition with -DBLAS64
(to assert the use of 64-bit integers in the BLAS) or -DBLAS32, (to assert the
use of 32-bit integers in the BLAS).
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
-DALLOW_64BIT_BLAS=1.
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 BLA_VENDOR, or set it to
ANY. In this case, if ALLOW_64BIT_BLAS is set, preference is given to a
64-bit BLAS, but a 32-bit BLAS library will be used if no 64-bit library is
found.
When selecting a particular BLAS library, the ALLOW_64BIT_BLAS setting is
strictly followed. If set to true, only a 64-bit BLAS library will be used.
If false (the default), only a 32-bit BLAS library will be used. If no such
BLAS is found, the build will fail.
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 optional, 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 needed if CUDA is not enabled.
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 needed if CUDA is not enabled.
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 CHOLMOD/SuiteSparse_metis directory. Its use is optional. To compile
CHOLMOD without it, use the CMAKE_OPTIONS="-DNPARTITION=1" setting. The use of
METIS can improve ordering quality for some matrices, particularly large 3D
discretizations. METIS has been slightly modified for use in SuiteSparse; see
the CHOLMOD/SuiteSparse_metis/README.txt file for details.
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, zstd, and xxHash (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.
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
SuiteSparse_install. All packages will be compiled, and several demos will be
run. To run a (long!) exhaustive test, do SuiteSparse_test.
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 SuiteSparse_paths m-file to
set all your paths at the start of each MATLAB session.
For Linux and Mac: type the following in this directory (requires system
priviledge to do the sudo make install):
make
sudo make install
All libraries will be created and copied into the default system-wide folder (/usr/local/lib on Linux). All include files need by the applications that use SuiteSparse are copied into /usr/local/include (on Linux).
For Windows, import each */CMakeLists.txt file into MS Visual Studio.
A single top-level CMake script is being considered as a feature in the
future. Be sure to specify the build type as Release; for example, to
build SuiteSparse_config on Windows in the command window:
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
(LD_LIBRARY_PATH in Linux), you can do the following (for example):
S = /home/me/SuiteSparse
cc myprogram.c -I$(S)/include -lumfpack -lamd -lcholmod -lsuitesparseconfig -lm
To change the C and C++ compilers, and to compile in parallel use:
CC=gcc CX=g++ JOBS=32 make
for example, which changes the compiler to gcc and g++, and runs make with 'make -j32', in parallel with 32 jobs.
This will work on Linux/Unix and the Mac. It should automatically detect if you have the Intel compilers or not, and whether or not you have CUDA.
NOTE: Use of the Intel MKL BLAS is strongly recommended. The OpenBLAS can
(rarely) result in severe performance degradation, in CHOLMOD in particular.
The reason for this is still under investigation and might already be resolved
in the current version of OpenBLAS. See
SuiteSparse_config/cmake_modules/SuiteSparsePolicy.cmake to select your BLAS.
You may also need to add SuiteSparse/lib to your path. If your copy of SuiteSparse is in /home/me/SuiteSparse, for example, then add this to your ~/.bashrc file:
LD_LIBRARY_PATH=$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
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
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 (because of -DNPARTITION=1). Debug mode will be used (the build type). The static libraries will not be built (since -DNSTATIC=1 is set).
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
CMAKE_INSTALL_PREFIX: defines the install location (default on Linux is
/usr/local). For example, this command in the top
level SuiteSparse folder will set the install directory
to "/stuff", used by the subsequent "sudo make install":
CMAKE_OPTIONS="-DCMAKE_INSTALL_PREFIX=/stuff" make
sudo make install
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.
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. I would also like to thank Raye Kimmerer for adding support for 32-bit row/column indices in SPQR v4.2.0.
See also the various Acknowledgements within each package.