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instead of static allocation in the nomalloc test. Uncomment commenetd parts of the nomalloc test since now matrix-matrix products are safe.
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Use JacobiSVD for now.
We do plan to reintroduce a bidiagonalizing SVD asap.
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- ensure static allocation for the product of "large" fixed size matrix
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As discussed on the list (too long to explain here).
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decomposition classes. One particular advantage of this is that decomposing matrices with max sizes known at compile time will not allocate.
NOTE: The ComplexEigenSolver class currently _does_ allocate (line 135 of Eigenvalues/ComplexEigenSolver.h), but the reason appears to be in the implementation of matrix-matrix products, and not in the decomposition itself.
The nomalloc unit test has been extended to verify that decompositions do not allocate when max sizes are specified. There are currently two workarounds to prevent the test from failing (see comments in test/nomalloc.cpp), both of which are related to matrix products that allocate on the stack.
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* renaming, e.g. LU ---> FullPivLU
* split tests framework: more robust, e.g. dont generate empty tests if a number is skipped
* make all remaining tests use that splitting, as needed.
* Fix 4x4 inversion (see stable branch)
* Transform::inverse() and geo_transform test : adapt to new inverse() API, it was also trying to instantiate inverse() for 3x4 matrices.
* CMakeLists: more robust regexp to parse the version number
* misc fixes in unit tests
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it never made very precise sense. but now does it still make any?
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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
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cachefriendlyproduct, that should be banned as well as depending on the
platform they can give a malloc, and they could happen even with (large
enough) fixed size matrices. Corresponding fix in Product.h:
cachefriendly is now only used for dynamic matrices -- fixedsize, no
matter how large, doesn't use the cachefriendly product. We don't need
to care (in my opinion) about performance for large fixed size, as large
fixed size is a bad idea in the first place and it is more important to
be able to guarantee clearly that fixed size never causes a malloc.
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* 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"...
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Renamed "MatrixBase::extract() const" to "MatrixBase::part() const"
* Renamed static functions identity, zero, ones, random with an upper case
first letter: Identity, Zero, Ones and Random.
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can be seen in Eigen/src/Core/Cwise.h.
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This is the first step towards a non-selfadjoint eigen solver.
Notes:
- We might consider merging Tridiagonalization and Hessenberg toghether ?
- Or we could factorize some code into a Householder class (could also be shared with QR)
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Benoit Jacob
Gael Guennebaud
Hauke Heibel
Adolfo Rodriguez Tsouroukdissian