make StdVector be a wrapper around it if EIGEN_USE_NEW_STDVECTOR is defined
otherwise StdVector doesn't change ---> compatibility is preserved
backport unit-test
My initial fix was incorrect, the libraries must be quoted when being
passed to the add test macro, but must be unquoted when passed to the
target_link_libraries function.
This fixes an issue where multiple versions of the Qt libraries are
available, if the Qt library variable is not quoted an error was
generated as only the first part 'optimized' was used by the create test
macro.
df9dfa145547529bf71afd4c6e8f3af947acaad0
This is what is needed to make Step (in KDE/kdeedu) build.
The rest of Eigen (outside of Sparse) is unaffected except for a few trivial changes that were needed.
calling this 2.0.3, will tag if no problem.
That means a lot of features which were available for sparse matrices
via the dense (and super slow) implemention are no longer available.
All features which make sense for sparse matrices (aka can be implemented efficiently) will be
implemented soon, but don't expect to see an API as rich as for the dense path.
Other changes:
* no block(), row(), col() anymore.
* instead use .innerVector() to get a col or row vector of a matrix.
* .segment(), start(), end() will be back soon, not sure for block()
* faster cwise product
*Add Eigen/StdVector header.
Including it #includes<vector> and "Core" and generates a partial
specialization of std::vector<T> for T=Eigen::Matrix<...>
that will work even with vectorizable fixed-size Eigen types
(working around a design issue in the c++ STL)
*Add unit-test
CCMAIL: alex.stapleton@gmail.com
* the dashboard is there: http://my.cdash.org/index.php?project=Eigen
* now you can run the tests from the top build dir
and submit report like that (from the top build dir):
ctest -D Experimental
* todo:
- add some nighlty builds (I'll add a few on my computer)
- add valgrind memory checks, performances tests, compilation time tests, etc.
* extend unit tests
* add support for generic sum reduction and dot product
* optimize the cwise()* : this is a special case of CwiseBinaryOp where
we only have to process the coeffs which are not null for *both* matrices.
Perhaps there exist some other binary operations like that ?
* fix issues in Product revealed by this test
* in Dot.h forbid mixing of different types (at least for now, might allow real.dot(complex) in the future).
* 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.
Some naming questions:
- for "extend" we could also think of: "expand", "union", "add"
- same for "clamp": "crop", "intersect"
- same for "contains": "isInside", "intersect"
=> ah "intersect" is conflicting, so that eliminates this one !
* add a WithAlignedOperatorNew class with overloaded operator new
* make Matrix (and Quaternion, Transform, Hyperplane, etc.) use it
if needed such that "*(new Vector4) = xpr" does not failed anymore.
* Please: make sure your classes having fixed size Eigen's vector
or matrice attributes inherit WithAlignedOperatorNew
* add a ei_new_allocator STL memory allocator to use with STL containers.
This allocator really calls operator new on your types (unlike GCC's
new_allocator). Example:
std::vector<Vector4f> data(10);
will segfault if the vectorization is enabled, instead use:
std::vector<Vector4f,ei_new_allocator<Vector4f> > data(10);
NOTE: you only have to worry if you deal with fixed-size matrix types
with "sizeof(matrix_type)%16==0"...
* added a meta.cpp unit test
* EIGEN_TUNE_FOR_L2_CACHE_SIZE now represents L2 block size in Bytes (whence the ei_meta_sqrt...)
* added a CustomizeEigen.dox page
* added a TOC to QuickStartGuide.dox
IoFormat OctaveFmt(4, AlignCols, ", ", ";\n", "", "", "[", "]");
cout << mat.format(OctaveFmt);
The first "4" is the precision.
Documentation missing.
* Some compilation fixes
- the decompostion code has been adfapted from JAMA
- handles non square matrices of size MxN with M>=N
- does not work for complex matrices
- includes a solver where the parts corresponding to zero singular values are set to zero
