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CXSparse: a Concise eXtended Sparse Matrix Package
CXSparse, Copyright (c) 2006-2022, Timothy A. Davis. All Rights Reserved.
SPDX-License-Identifier: LGPL-2.1+
Derived from CSparse. Conversion originally by David Bateman, Motorola,
and then modified by Tim Davis. ANSI C11 is required, with support for
the double complex data type.
(if you use a C++ compiler, the C++ complex type is used instead).
CXSparse is a version of CSparse that operates on both real and complex
matrices, using either int32_t or int64_t integers.
To install for use in MATLAB, simply type "cs_install" in the MATLAB Command
Window, while in the CXSparse/MATLAB directory. (NOTE: you may need to
run "mex -setup" first).
Refer to "Direct Methods for Sparse Linear Systems," Timothy A. Davis,
SIAM, Philadelphia, 2006. No detailed user guide is included in this
package; the user guide is the book itself.
To compile the C-library (./Source) and C demo programs (./Demo) just type
"make" in this directory, or import the CMakeLists.txt into your build system
(such as Microsoft Visual Studio). To install, use "sudo make install".
Run the demos with "make demos". To clean up, do "make clean".
To run the exhaustive tests, type "make" in the Tcov directory (Linux is
assumed). To remove all files not in the original distribution, type "make
distclean".
While in MATLAB, type "cs_install" while in the CSparse/MATLAB directory to
compile and install CSparse for use in MATLAB. For more details, see
CXSparse/MATLAB/README.txt.
This package is backward compatible with CSparse. That is, user code that
uses CSparse may switch to using CXSparse without any changes to the user code.
Each CXSparse function has a generic version with the same name as the CSparse
function, and four type-specific versions. For example:
cs_add same as cs_add_di by default, but can be changed to use
int64_t
integers if user code is compiled with -DCS_LONG, and/or can
be changed to operate on complex matrices with -DCS_COMPLEX.
cs_di_add double/int version of cs_add
cs_dl_add double/int64_t version of cs_add
cs_ci_add complex/int version of cs_add
cs_cl_add complex/int64_t version of cs_add
The sparse matrix data structures are treated in the same way: cs, css,
csn, and csd become cs_di, cs_dis, cs_din, and cs_did for the double/int case,
cs_cl, cs_cls, cs_cln, and cs_cld for the complex/int64_t case, and so
on.
See cs_demo.c for a type-generic user program, and cs_cl_demo.c for a
type-specific version of the same program (complex/int64_t).
Several macros are available in CXSparse (but not in CSparse) to allow user
code to be written in a type-generic manner:
CS_INT int by default, int64_t if -DCS_LONG compiler flag
is used
CS_ENTRY double by default, double complex if -DCS_COMPLEX flag is used.
CS_ID "%d" or "%"PRId64, for printf and scanf of the CS_INT type.
CS_INT_MAX INT32_MAX or INT64_MAX, the largest possible value of CS_INT.
CS_REAL(x) x or creal(x)
CS_IMAG(x) 0 or cimag(x)
CS_CONJ(x) x or conj(x)
CS_ABS(x) fabs(x) or cabs(x)
Even the name of the include file (cs.h) is the same. To use CXSparse instead
of CSparse, simply compile with -ICXSparse/Source instead of -ICSparse/Source,
and link against libcxsparse.a instead of the CSparse libcsparse.a library.
To determine at compile time if CXSparse or CSparse is being used:
#ifdef CXSPARSE
CXSparse is in use. The generic functions equivalent to CSparse may
be used (cs_add, etc). These generic functions can use different
types, depending on the -DCS_LONG and -DCS_COMPLEX compile flags, with
the default being double/int. The type-specific functions and data
types (cs_di_add, cs_di, CS_INT, etc.) can be used.
#else
CSparse is in use. Only the generic functions "cs_add", etc., are
available, and they are of type double/int.
#endif
See cs.h for the prototypes of each function, and the book "Direct Methods
for Sparse Linear Systems" for full documentation of CSparse and CXSparse.
Other changes from CSparse: cs_transpose performs the complex conjugate
transpose if 'values' input parameter is >0 (C=A'), the pattern-only transpose
if values=0 (C=spones(A') in MATLAB), and the array transpose if values<0
(C=A.' in MATLAB notation). A set of four conversion routines are included in
CXSparse, to convert real matrices to/from complex matrices. The Householder
reflection constructed by cs_house.c also differs slightly, to accomodate both
the real and complex cases properly.
CXSparse is generated automatically from CSparse. Refer to
http://www.suitesparse.com for details.
--------------------------------------------------------------------------------
Contents:
--------------------------------------------------------------------------------
Demo/ demo C programs that use CXSparse
Doc/ license and change log
Makefile Makefile for the whole package
Makefile Makefile for the whole package (optional)
Matrix/ sample matrices (with extra complex matrices for CXSparse)
README.txt this file
Source/ primary CXSparse source files
Tcov/ CXSparse tests
Config/ source for Include/cs.h
build/ compiled files
--------------------------------------------------------------------------------
./Doc: license and change log
--------------------------------------------------------------------------------
ChangeLog changes in CSparse since first release
License.txt license
--------------------------------------------------------------------------------
./Source: Primary source code for CXSparse
--------------------------------------------------------------------------------
cs_add.c add sparse matrices
cs_amd.c approximate minimum degree
cs_chol.c sparse Cholesky
cs_cholsol.c x=A\b using sparse Cholesky
cs_compress.c convert a triplet form to compressed-column form
cs_counts.c column counts for Cholesky and QR
cs_convert.c convert real to complex and complex to real (not in CSparse)
cs_cumsum.c cumulative sum
cs_dfs.c depth-first-search
cs_dmperm.c Dulmage-Mendelsohn permutation
cs_droptol.c drop small entries from a sparse matrix
cs_dropzeros.c drop zeros from a sparse matrix
cs_dupl.c remove (and sum) duplicates
cs_entry.c add an entry to a triplet matrix
cs_ereach.c nonzero pattern of Cholesky L(k,:) from etree and triu(A(:,k))
cs_etree.c find elimination tree
cs_fkeep.c drop entries from a sparse matrix
cs_gaxpy.c sparse matrix times dense matrix
cs.h include file for CXSparse
cs_happly.c apply Householder reflection
cs_house.c Householder reflection (*** NOTE: different algo. from CSparse)
cs_ipvec.c x(p)=b
cs_leaf.c determine if j is a leaf of the skeleton matrix and find lca
cs_load.c load a sparse matrix from a file
cs_lsolve.c x=L\b
cs_ltsolve.c x=L'\b
cs_lu.c sparse LU factorization
cs_lusol.c x=A\b using sparse LU factorization
cs_malloc.c memory manager
cs_maxtrans.c maximum transveral (permutation for zero-free diagonal)
cs_multiply.c sparse matrix multiply
cs_norm.c sparse matrix norm
cs_permute.c permute a sparse matrix
cs_pinv.c invert a permutation vector
cs_post.c postorder an elimination tree
cs_print.c print a sparse matrix
cs_pvec.c x=b(p)
cs_qr.c sparse QR
cs_qrsol.c solve a least-squares problem
cs_randperm.c random permutation
cs_reach.c find nonzero pattern of x=L\b for sparse L and b
cs_scatter.c scatter a sparse vector
cs_scc.c strongly-connected components
cs_schol.c symbolic Cholesky
cs_spsolve.c x=Z\b where Z, x, and b are sparse, and Z upper/lower triangular
cs_sqr.c symbolic QR (also can be used for LU)
cs_symperm.c symmetric permutation of a sparse matrix
cs_tdfs.c depth-first-search of a tree
cs_transpose.c transpose a sparse matrix
cs_updown.c sparse rank-1 Cholesky update/downate
cs_usolve.c x=U\b
cs_util.c various utilities (allocate/free matrices, workspace, etc)
cs_utsolve.c x=U'\b
README.txt README file for CXSparse/Source
cs_*_ci.c complex int32_t versions
cs_*_cl.c complex int64_t versions
cs_*_dl.c double int64_t versions
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./Config:
--------------------------------------------------------------------------------
cs.h.in source for cs.h
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./Demo: C program demos
--------------------------------------------------------------------------------
cs_ci_demo1.c complex/int version of cs_demo1.c
cs_ci_demo2.c complex/int version of cs_demo2.c
cs_ci_demo3.c complex/int version of cs_demo3.c
cs_ci_demo.c complex/int version of cs_demo.c
cs_ci_demo.h complex/int version of cs_demo.h
cs_cl_demo1.c complex/int64_t version of cs_demo1.c
cs_cl_demo2.c complex/int64_t version of cs_demo2.c
cs_cl_demo3.c complex/int64_t version of cs_demo3.c
cs_cl_demo.c complex/int64_t version of cs_demo.c
cs_cl_demo.h complex/int64_t version of cs_demo.h
cs_demo1.c read a matrix from a file and perform basic matrix operations
cs_demo2.c read a matrix from a file and solve a linear system
cs_demo3.c read a matrix, solve a linear system, update/downdate
cs_demo.c support routines for cs_demo*.c
cs_demo.h include file for demo programs
cs_demo.out output of "make", which runs the demos on some matrices
cs_di_demo1.c double/int version of cs_demo1.c
cs_di_demo2.c double/int version of cs_demo2.c
cs_di_demo3.c double/int version of cs_demo3.c
cs_di_demo.c double/int version of cs_demo.c
cs_di_demo.h double/int version of cs_demo.h
cs_dl_demo1.c double/int64_t version of cs_demo1.c
cs_dl_demo2.c double/int64_t version of cs_demo2.c
cs_dl_demo3.c double/int64_t version of cs_demo3.c
cs_dl_demo.c double/int64_t version of cs_demo.c
cs_dl_demo.h double/int64_t version of cs_demo.h
cs_idemo.c convert real matrices to/from complex (int version)
cs_ldemo.c convert real matrices to/from complex (int64_t version)
README.txt Demo README file
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./MATLAB: MATLAB interface, demos, and tests
--------------------------------------------------------------------------------
cs_install.m MATLAB function for compiling and installing CSparse for MATLAB
CSparse/ MATLAB interface for CSparse
Demo/ MATLAB demos for CSparse
Makefile MATLAB interface Makefile
README.txt MATLAB README file
Test/ MATLAB test for CSparse, and "textbook" routines
ssget/ MATLAB interface to SuiteSparse Sparse Matrix Collection
--------------------------------------------------------------------------------
./MATLAB/CSparse: MATLAB interface for CSparse
--------------------------------------------------------------------------------
Contents.m Contents of MATLAB interface to CSparse
cs_add.m add two sparse matrices
cs_add_mex.c
cs_amd.m approximate minimum degree
cs_amd_mex.c
cs_chol.m sparse Cholesky
cs_chol_mex.c
cs_cholsol.m x=A\b using a sparse Cholesky
cs_cholsol_mex.c
cs_counts.m column counts for Cholesky or QR (like "symbfact" in MATLAB)
cs_counts_mex.c
cs_dmperm.m Dulmage-Mendelsohn permutation
cs_dmperm_mex.c
cs_dmsol.m x=A\b using dmperm
cs_dmspy.m plot a picture of a dmperm-permuted matrix
cs_droptol.m drop small entries
cs_droptol_mex.c
cs_esep.m find edge separator
cs_etree.m compute elimination tree
cs_etree_mex.c
cs_gaxpy.m sparse matrix times dense vector
cs_gaxpy_mex.c
cs_lsolve.m x=L\b where L is lower triangular
cs_lsolve_mex.c
cs_ltsolve.m x=L'\b where L is lower triangular
cs_ltsolve_mex.c
cs_lu.m sparse LU factorization
cs_lu_mex.c
cs_lusol.m x=A\b using sparse LU factorization
cs_lusol_mex.c
cs_make.m compiles CSparse for use in MATLAB
cs_mex.c support routines for CSparse mexFunctions
cs_mex.h
cs_multiply.m sparse matrix multiply
cs_multiply_mex.c
cs_must_compile.m determine if a source file needs to be compiled with mex
cs_nd.m nested dissection
cs_nsep.m find node separator
cs_permute.m permute a sparse matrix
cs_permute_mex.c
cs_print.m print a sparse matrix
cs_print_mex.c
cs_qleft.m apply Householder vectors to the left
cs_qright.m apply Householder vectors to the right
cs_qr.m sparse QR factorization
cs_qr_mex.c
cs_qrsol.m solve a sparse least squares problem
cs_qrsol_mex.c
cs_randperm.m randdom permutation
cs_randperm_mex.c
cs_scc.m strongly-connected components
cs_scc_mex.c
cs_sep.m convert an edge separator into a node separator
cs_sparse.m convert a triplet form matrix to a compress-column form
cs_sparse_mex.c
cs_symperm.m symmetric permutation of a sparse matrix
cs_symperm_mex.c
cs_sqr.m symbolic QR ordering and analysis
cs_sqr_mex.c
cs_thumb_mex.c compute small "thumbnail" of a sparse matrix (for cspy).
cs_transpose.m transpose a sparse matrix
cs_transpose_mex.c
cs_updown.m sparse Cholesky update/downdate
cs_updown_mex.c
cs_usolve.m x=U\b where U is upper triangular
cs_usolve_mex.c
cs_utsolve.m x=U'\b where U is upper triangular
cs_utsolve_mex.c
cspy.m a color "spy"
Makefile Makefile for CSparse MATLAB interface
README.txt README file for CSparse MATLAB interface
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./MATLAB/Demo: MATLAB demos for CSparse
--------------------------------------------------------------------------------
Contents.m Contents of MATLAB demo for CSparse
cs_demo.m run all MATLAB demos for CSparse
cs_demo1.m MATLAB version of Demo/cs_demo1.c
cs_demo2.m MATLAB version of Demo/cs_demo2.c
cs_demo3.m MATLAB version of Demo/cs_demo3.c
private/ private functions for MATLAB demos
README.txt README file for CSparse MATLAB demo
--------------------------------------------------------------------------------
./MATLAB/Demo/private: private functions for MATLAB demos
--------------------------------------------------------------------------------
demo2.m demo 2
demo3.m demo 3
ex_1.m example 1
ex2.m example 2
ex3.m example 3
frand.m generate a random finite-element matrix
get_problem.m get a matrix
is_sym.m determine if a matrix is symmetric
mesh2d1.m construct a 2D mesh (method 1)
mesh2d2.m construct a 2D mesh (method 2)
mesh3d1.m construct a 3D mesh (method 1)
mesh3d2.m construct a 3D mesh (method 2)
print_order.m print the ordering method used
resid.m compute residual
rhs.m create right-hand-side
--------------------------------------------------------------------------------
./MATLAB/Test: Extensive test of CSparse, in MATLAB
--------------------------------------------------------------------------------
Makefile Makefile for MATLAB Test directory
README.txt README file for MATLAB/Test
Contents.m Contents of MATLAB/Test, "textbook" files only
chol_downdate.m downdate a Cholesky factorization.
chol_left.m left-looking Cholesky factorization.
chol_left2.m left-looking Cholesky factorization, more details.
chol_right.m right-looking Cholesky factorization.
chol_super.m left-looking "supernodal" Cholesky factorization.
chol_up.m up-looking Cholesky factorization.
chol_update.m update a Cholesky factorization.
chol_updown.m update or downdate a Cholesky factorization.
cond1est.m 1-norm condition estimate.
cs_fiedler.m the Fiedler vector of a connected graph.
givens2.m find a Givens rotation.
house.m find a Householder reflection.
lu_left.m left-looking LU factorization.
lu_right.m right-looking LU factorization.
lu_rightp.m right-looking LU factorization, with partial pivoting.
lu_rightpr.m recursive right-looking LU, with partial pivoting.
lu_rightr.m recursive right-looking LU.
norm1est.m 1-norm estimate.
qr_givens.m Givens-rotation QR factorization.
qr_givens_full.m Givens-rotation QR factorization, for full matrices.
qr_left.m left-looking Householder QR factorization.
qr_right.m right-looking Householder QR factorization.
cs_fiedler.m Fiedler vector
cs_frand.m generate a random finite-element matrix
cs_frand_mex.c
cs_ipvec.m x(p)=b
cs_ipvec_mex.c
cs_maxtransr.m recursive maximum matching algorithm
cs_maxtransr_mex.c
cs_pvec.m x=b(p)
cs_pvec_mex.c interface for cs_pvec
cs_reach.m non-recursive reach (interface to CSparse cs_reach)
cs_reach_mex.c non-recursive x=spones(L\sparse(b))
cs_reachr.m recursive reach (interface to CSparse cs_reachr)
cs_reachr_mex.c
cs_rowcnt.m row counts for sparse Cholesky
cs_rowcnt_mex.c row counts for sparse Cholesky
cs_sparse2.m same as cs_sparse, to test cs_entry function
cs_sparse2_mex.c like cs_sparse, but for testing cs_entry
cs_test_make.m compiles MATLAB tests
check_if_same.m check if two inputs are identical or not
choldn.m Cholesky downdate
cholup.m Cholesky update, using Given's rotations
cholupdown.m Cholesky update/downdate (Bischof, Pan, and Tang method)
cs_q1.m construct Q from Householder vectors
cs_test_make.m compiles the CSparse, Demo, and Test mexFunctions.
dmperm_test.m test cs_dmperm
chol_example.m simple Cholesky factorization example
etree_sample.m construct a sample etree and symbolic factorization
gqr3.m QR factorization, based on Givens rotations
happly.m apply Householder reflection to a vector
hmake1.m construct a Householder reflection
mynormest1.m estimate norm(A,1), using LU factorization (L*U = P*A*Q).
myqr.m QR factorization using Householder reflections
another_colormap.m try another color map
cspy_test.m test cspy and cs_dmspy
qr2.m QR factorization based on Householder reflections
sample_colormap.m try a colormap for use in cspy
signum.m compute and display the sign of a column vector x
sqr_example.m test cs_sqr
dmspy_test.m test cspy, cs_dmspy, and cs_dmperm
test_qr.m test various QR factorization methods
test_randperms.m test random permutations
testh.m test Householder reflections
test_qr1.m test QR factorizations
test_qrsol.m test cs_qrsol
test_sep.m test cs_sep, and compare with Gilbert's meshpart vtxsep
testall.m test all CSparse functions (run tests 1 to 28 below)
test1.m test cs_transpose
test2.m test cs_sparse
test3.m test cs_lsolve, cs_ltsolve, cs_usolve, cs_chol
test4.m test cs_multiply
test5.m test cs_add
test6.m test cs_reach, cs_reachr, cs_lsolve, cs_usolve
test7.m test cs_lu
test8.m test cs_cholsol, cs_lusol
test9.m test cs_qr
test10.m test cs_qr
test11.m test cs_rowcnt
test12.m test cs_qr and compare with svd
test13.m test cs_counts, cs_etree
test14.m test cs_droptol
test15.m test cs_amd
test16.m test cs_amd
test17.m test cs_qr, cs_qright, cs_q1, cs_qrleft, cs_qrsol
test18.m test iterative refinement after backslash
test19.m test cs_dmperm, cs_maxtransr, cs_dmspy, cs_scc
test20.m test cholupdown
test21.m test cs_updown
test22.m test cond1est
test23.m test cs_dmspy
test24.m test cs_fielder
test25.m test cs_nd
test26.m test cs_dmsol and cs_dmspy
test27.m test cs_qr, cs_utsolve, cs_qrsol
test28.m test cs_randperm, cs_dmperm
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./MATLAB/ssget: MATLAB interface for the SuiteSparse Matrix Collection
--------------------------------------------------------------------------------
See MATLAB/ssget/README.txt for details
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./Matrix: Sample matrices, most from Rutherford/Boeing collection
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ash219 overdetermined pattern of Holland survey. Ashkenazi, 1974.
bcsstk01 stiffness matrix for small generalized eigenvalue problem
bcsstk16 stiffness matrix, Corp of Engineers dam
fs_183_1 unsymmetric facsimile convergence matrix
lp_afiro NETLIB afiro linear programming problem
mbeacxc US economy, 1972. Dan Szyld, while at NYU
t1 small example used in Chapter 2
west0067 Cavett problem with 5 components (chemical eng., Westerberg)
c_mbeacxc complex version of mbeacxc
c_west0067 complex version of west0067
mhd1280b Alfven spectra in magnetohydrodynamics (complex)
neumann complex matrix
qc324 model of H+ in an electromagnetic field (complex)
t2 small complex matrix
t3 small complex matrix
t4 small complex matrix
c4 small complex matrix
young1c aeronautical problem (complex matrix)
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./Tcov: Exhaustive test coverage of CXSparse
--------------------------------------------------------------------------------
covall same as covall.linux
cov.awk coverage summary
cover print uncovered lines
covs print uncovered lines
cstcov_malloc_test*.c malloc test
cstcov_malloc_test.h
cstcov_test*.c main programs for Tcov tests
gcovs run gcov (Linux)
Makefile Makefile for Tcov tests
nil an empty matrix
zero a 1-by-1 zero matrix
czero a 1-by-1 complex zero matrix
README.txt README file for Tcov directory
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