ei_aligned_malloc now really behaves like a malloc
(untyped, doesn't call ctor)
ei_aligned_new is the typed variant calling ctor
EIGEN_MAKE_ALIGNED_OPERATOR_NEW now takes the class name as parameter
order, one bit for enabling/disabling auto-alignment. If you want to
disable, do:
Matrix<float,4,1,Matrix_DontAlign>
The Matrix_ prefix is the only way I can see to avoid
ambiguity/pollution. The old RowMajor, ColMajor constants are
deprecated, remain for now.
* this prompted several improvements in matrix_storage. ei_aligned_array
renamed to ei_matrix_array and moved there. The %16==0 tests are now
much more centralized in 1 place there.
* unalignedassert test: updated
* update FindEigen2.cmake from KDElibs
* determinant test: use VERIFY_IS_APPROX to fix false positives; add
testing of 1 big matrix
* 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.
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 !
* rename Cholesky to LLT
* rename CholeskyWithoutSquareRoot to LDLT
* rename MatrixBase::cholesky() to llt()
* rename MatrixBase::choleskyNoSqrt() to ldlt()
* make {LLT,LDLT}::solve() API consistent with other modules
Note that we are going to keep a source compatibility untill the next beta release.
E.g., the "old" Cholesky* classes, etc are still available for some time.
To be clear, Eigen beta2 should be (hopefully) source compatible with beta1,
and so beta2 will contain all the deprecated API of beta1. Those features marked
as deprecated will be removed in beta3 (or in the final 2.0 if there is no beta 3 !).
Also includes various updated in sparse Cholesky.
all per plot settings have been moved to a single file, go_mean now takes an
optional second argument "tiny" to generate plots for tiny matrices, and
output of comparison information wrt to previous benchs (if any).
- remove all invertibility checking, will be redundant with LU
- general case: adapt to matrix storage order for better perf
- size 4 case: handle corner cases without falling back to gen case.
- rationalize with selectors instead of compile time if
- add C-style computeInverse()
* update inverse test.
* in snippets, default cout precision to 3 decimal places
* add some cmake module from kdelibs to support btl with cmake 2.4
- removed the ugly X11 and PNG gnuplots terminals
- use enhanced postscript terminal
- use imagemagick to generate the png files (with compression)
- disable the fortran impl by default since it is as meaningless as a "C impl"
- update line settings
It basically performs 4 dot products at once reducing loads of the vector and improving
instructions scheduling. With 3 cache friendly algorithms, we now handle all product
configurations with outstanding perf for large matrices.
the modifications to initial code follow:
* changed build system from plain makefiles to cmake
* added eigen2 (4 versions: vec/novec and fixed/dynamic), GMM++, MTL4 interfaces
* added "transposed matrix * vector" product action
* updated blitz interface to use condensed products instead of hand coded loops
* removed some deprecated interfaces
* changed default storage order to column major for all libraries
* new generic bench timer strategy which is supposed to be more accurate
* various code clean-up
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
* 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 !
* 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
