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add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
#ifndef EIGEN_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
#include "Core"
Patch by Kenneth Riddile: disable MSVC warnings, reenable them outside of Eigen, and add a MSVC-friendly path in StaticAssert.
2008-12-18 20:48:02 +00:00
#include "src/Core/util/DisableMSVCWarnings.h"
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
#include <vector>
#include <map>
fix a number of compiler errors and warnings with gcc 4.3. There was a missing #include<algorithm> so I'm not sure how it compiled at all for you :)
2008-08-22 01:19:53 +00:00
#include <cstdlib>
#include <cstring>
#include <algorithm>
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
sparse module: - remove some useless stuff => let's focus on a single sparse matrix format - finalize the new RandomSetter
2008-10-21 13:35:04 +00:00
#ifdef EIGEN_GOOGLEHASH_SUPPORT
#include <google/dense_hash_map>
#endif
Sparse module: add experimental support for TAUCS and CHOLMOD with: * bidirectionnal mapping * full cholesky factorization
2008-10-05 13:38:38 +00:00
#ifdef EIGEN_CHOLMOD_SUPPORT
extern "C" {
#include "cholmod.h"
}
#endif
#ifdef EIGEN_TAUCS_SUPPORT
* add cmake files to find (optional) supported libraries * add unit tests for sparse cholesky
2008-10-20 10:43:11 +00:00
// taucs.h declares a lot of mess
#define isnan
#define finite
#define isinf
Sparse module: add experimental support for TAUCS and CHOLMOD with: * bidirectionnal mapping * full cholesky factorization
2008-10-05 13:38:38 +00:00
extern "C" {
#include "taucs.h"
}
* add cmake files to find (optional) supported libraries * add unit tests for sparse cholesky
2008-10-20 10:43:11 +00:00
#undef isnan
#undef finite
#undef isinf
Sparse module: add experimental support for TAUCS and CHOLMOD with: * bidirectionnal mapping * full cholesky factorization
2008-10-05 13:38:38 +00:00
#ifdef min
#undef min
#endif
#ifdef max
#undef max
#endif
#endif
sparse module: add preliminary support for direct sparse LU solver using SuperLU. Calling SuperLU was very painful, but it was worth it, it seems to be damn fast !
2008-10-19 15:26:28 +00:00
#ifdef EIGEN_SUPERLU_SUPPORT
typedef int int_t;
#include "superlu/slu_Cnames.h"
#include "superlu/supermatrix.h"
#include "superlu/slu_util.h"
namespace SuperLU_S {
#include "superlu/slu_sdefs.h"
}
namespace SuperLU_D {
#include "superlu/slu_ddefs.h"
}
namespace SuperLU_C {
#include "superlu/slu_cdefs.h"
}
namespace SuperLU_Z {
#include "superlu/slu_zdefs.h"
}
namespace Eigen { struct SluMatrix; }
#endif
sparse module: add support for umfpack, the sparse direct LU solver from suitesparse (as cholmod). It seems to be even faster than SuperLU and it was much simpler to interface ! Well, the factorization is faster, but for the solve part, SuperLU is quite faster. On the other hand the solve part represents only a fraction of the whole procedure. Moreover, I bench random matrices that does not represents real cases, and I'm not sure at all I use both libraries with their best settings !
2008-10-19 22:44:21 +00:00
#ifdef EIGEN_UMFPACK_SUPPORT
#include "umfpack.h"
#endif
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
namespace Eigen {
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
#include "src/Sparse/SparseUtil.h"
#include "src/Sparse/SparseMatrixBase.h"
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
#include "src/Sparse/SparseArray.h"
Sparse module: * several fixes (transpose, matrix product, etc...) * Added a basic cholesky factorization * Added a low level hybrid dense/sparse vector class to help writing code involving intensive read/write in a fixed vector. It is currently used to implement the matrix product itself as well as in the Cholesky factorization.
2008-10-04 14:23:00 +00:00
#include "src/Sparse/AmbiVector.h"
sparse module: add a RandomSetter based on a user defined map implementation as described on the wiki (one map per N column) Here's some bench results for the 4 currently supported map impl: std::map => 18.3385 (581 MB) gnu::hash_map => 6.52574 (555 MB) google::dense => 2.87982 (315 MB) google::sparse => 15.7441 (165 MB) This is the time is second (and memory consumption) to insert/lookup 10 million of coeffs with random coords inside a 10000^2 matrix, with one map per packet of 64 columns => google::dense really rocks ! Note I use for the key value the index of the column in the packet (between 0 and 63) times the number of rows and I used the default hash function.... so maybe there is room for improvement here....
2008-10-20 23:42:20 +00:00
#include "src/Sparse/RandomSetter.h"
Extended sparse unit-test: nested blocks and InnerIterators. Block specialization for sparse matrices. InnerIterators for Blocks and fixes in CoreIterators.
2008-09-02 15:28:49 +00:00
#include "src/Sparse/SparseBlock.h"
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
#include "src/Sparse/SparseMatrix.h"
Added a SparseVector class (not tested yet)
2009-01-02 08:47:09 +00:00
#include "src/Sparse/SparseVector.h"
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
#include "src/Sparse/CoreIterators.h"
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
#include "src/Sparse/SparseProduct.h"
* added an in-place version of inverseProduct which might be twice faster fot small fixed size matrix * added a sparse triangular solver (sparse version of inverseProduct) * various other improvements in the Sparse module
2008-06-29 21:29:12 +00:00
#include "src/Sparse/TriangularSolver.h"
sparse module: added some documentation for the LLT solver
2008-10-18 18:33:56 +00:00
#include "src/Sparse/SparseLLT.h"
Several improvements in sparse module: * add a LDL^T factorization with solver using code from T. Davis's LDL library (LPGL2.1+) * various bug fixes in trianfular solver, matrix product, etc. * improve cmake files for the supported libraries * split the sparse unit test * etc.
2008-11-05 13:47:55 +00:00
#include "src/Sparse/SparseLDLT.h"
sparse module: added some documentation for the LLT solver
2008-10-18 18:33:56 +00:00
#include "src/Sparse/SparseLU.h"
Sparse module: add experimental support for TAUCS and CHOLMOD with: * bidirectionnal mapping * full cholesky factorization
2008-10-05 13:38:38 +00:00
#ifdef EIGEN_CHOLMOD_SUPPORT
# include "src/Sparse/CholmodSupport.h"
#endif
#ifdef EIGEN_TAUCS_SUPPORT
# include "src/Sparse/TaucsSupport.h"
#endif
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
sparse module: add preliminary support for direct sparse LU solver using SuperLU. Calling SuperLU was very painful, but it was worth it, it seems to be damn fast !
2008-10-19 15:26:28 +00:00
#ifdef EIGEN_SUPERLU_SUPPORT
# include "src/Sparse/SuperLUSupport.h"
#endif
sparse module: add support for umfpack, the sparse direct LU solver from suitesparse (as cholmod). It seems to be even faster than SuperLU and it was much simpler to interface ! Well, the factorization is faster, but for the solve part, SuperLU is quite faster. On the other hand the solve part represents only a fraction of the whole procedure. Moreover, I bench random matrices that does not represents real cases, and I'm not sure at all I use both libraries with their best settings !
2008-10-19 22:44:21 +00:00
#ifdef EIGEN_UMFPACK_SUPPORT
# include "src/Sparse/UmfPackSupport.h"
#endif
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
} // namespace Eigen
Patch by Kenneth Riddile: disable MSVC warnings, reenable them outside of Eigen, and add a MSVC-friendly path in StaticAssert.
2008-12-18 20:48:02 +00:00
#include "src/Core/util/EnableMSVCWarnings.h"
add experimental code for sparse matrix: - uses the common "Compressed Column Storage" scheme - supports every unary and binary operators with xpr template assuming binaryOp(0,0) == 0 and unaryOp(0) = 0 (otherwise a sparse matrix doesnot make sense) - this is the first commit, so of course, there are still several shorcommings !
2008-06-23 13:25:22 +00:00
#endif // EIGEN_SPARSE_MODULE_H
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