CXSparse: Copyright (c) 2006-2012, Timothy A. Davis.
http://www.suitesparse.com
Derived from CSparse. Conversion originally by David Bateman, Motorola,
and then modified by Tim Davis. ANSI C99 is required, with support for
the _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 int or SuiteSparse_long integers. A SuiteSparse_long is
normally just a long on most platforms, but becomes __int64 on WIN64. It now
includes a MATLAB interface, enabling the use of CXSparse functions on both
32-bit and 64-bit platforms.
To install for use in MATLAB, simply type "cs_install" in the MATLAB Command
Window, while in the CXSparse/MATLAB directory. (NOTE: Windows users cannot
use the "lcc" command; run "mex -setup" first, and select a different
compiler). If you use the Unix "make" command in that directory instead and
are using a 64-bit platform, then you must edit the CXSparse/MATLAB/Makefile
first. Refer to the instructions in that file.
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), C demo programs (./Demo) just type "make"
in this directory. To run the exhaustive statement coverage tests, type
"make" in the Tcov directory; the Tcov tests assume you are using Linux. To
remove all files not in the original distribution, type "make distclean".
I recommend that you use a different level of
optimization than "cc -O", which was chosen so that the Makefile is portable.
See Source/Makefile.
If your C compiler does not support the ANSI C99 complex type, the
#include <complex.h> statement will fail. If this happens, compile the
code with the -DNCOMPLEX flag (the MATLAB cs_install will do this for you).
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
SuiteSparse_long
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/SuiteSparse_long version of cs_add
cs_ci_add complex/int version of cs_add
cs_cl_add complex/SuiteSparse_long 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/SuiteSparse_long 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/SuiteSparse_long).
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, SuiteSparse_long if -DCS_LONG compiler flag
is used
CS_ENTRY double by default, double complex if -DCS_COMPLEX flag is used.
CS_ID "%d" or "%ld", for printf and scanf of the CS_INT type.
CS_INT_MAX INT_MAX or LONG_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>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
MATLAB/ MATLAB interface, demos, and tests for CXSparse
Matrix/ sample matrices (with extra complex matrices for CXSparse)
README.txt this file
Source/ primary CXSparse source files
Tcov/ CXSparse tests
--------------------------------------------------------------------------------
./Doc: license and change log
--------------------------------------------------------------------------------
ChangeLog changes in CSparse since first release
lesser.txt the GNU LGPL
License.txt license (GNU LGPL)
--------------------------------------------------------------------------------
./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 compress 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
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suitesparse-metis-for-windows-master (2000个子文件)
gkregex.c 304KB
ut.c 246KB
ccolamd.c 138KB
null2.c 108KB
colamd.c 106KB
RBio.c 92KB
umf_colamd.c 91KB
umfpack_qsymbolic.c 89KB
klu_mex.c 78KB
camd_2.c 69KB
cholmod_nesdis.c 68KB
cholmod_check.c 65KB
amd_2.c 63KB
kwayfm.c 62KB
csr.c 61KB
umfpackmex.c 58KB
umf_local_search.c 57KB
cholmod_matlab.c 53KB
cholmod_updown.c 51KB
cholmod_solve.c 49KB
graph.c 48KB
solve.c 48KB
t_cholmod_gpu.c 47KB
cm.c 47KB
klutest.c 43KB
t_cholmod_super_numeric.c 42KB
cholmod_read.c 40KB
cholmod_change_factor.c 38KB
klu_kernel.c 33KB
cholmod_super_symbolic.c 33KB
umf_assemble.c 33KB
umf_solve.c 33KB
cholmod_analyze.c 32KB
coarsen.c 32KB
umf_dump.c 32KB
cholmod_transpose.c 31KB
getopt.c 30KB
test_ops.c 29KB
umfpack_xx_demo.c 29KB
umf_kernel_init.c 29KB
umfpack_zl_demo.c 28KB
umfpack_numeric.c 28KB
umfpack_zi_demo.c 28KB
umfpack_dl_demo.c 27KB
cholmod_factor.c 27KB
umfpack_get_numeric.c 27KB
umfpack_di_demo.c 26KB
umf_singletons.c 26KB
t_cholmod_lsolve.c 26KB
cholmod_metis.c 26KB
umf_store_lu.c 26KB
lpdemo.c 26KB
cholmod_rowfac.c 24KB
klu.c 24KB
cholmod_common.c 24KB
btf_strongcomp.c 24KB
raw_factor.c 23KB
cholmod_triplet.c 23KB
cholmod_l_demo.c 23KB
minconn.c 23KB
umf_row_search.c 23KB
t_cholmod_ltsolve.c 22KB
parmetis.c 22KB
t_cholmod_updown_numkr.c 21KB
contig.c 21KB
umf_garbage_collection.c 21KB
mmd.c 21KB
cholmod_write.c 21KB
umfpack_report_info.c 20KB
ometis.c 20KB
ldl.c 20KB
umf_analyze.c 20KB
cholmod_demo.c 20KB
cholmod_dense.c 20KB
kwayrefine.c 20KB
sfm.c 20KB
cholmod_rowadd.c 19KB
umf4_f77wrapper.c 19KB
fm.c 18KB
t_cholmod_sdmult.c 18KB
klu_factor.c 18KB
cholmod_resymbol.c 18KB
cholmod_rowcolcounts.c 18KB
initpart.c 18KB
options.c 18KB
cholmod_sparse.c 17KB
umf4.c 17KB
umfpack_report_numeric.c 17KB
io.c 17KB
umf_create_element.c 17KB
klu_refactor.c 17KB
cholmod_memory.c 17KB
umf4_f77zwrapper.c 17KB
ssmult_saxpy.c 17KB
klu_analyze.c 16KB
klu_diagnostics.c 16KB
cs_amd.c 16KB
cholmod_symmetry.c 16KB
cs_amd.c 16KB
balance.c 16KB
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