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(as new members to SelfAdjointEigenSolver)
The QR module now depends on Cholesky.
* Fix Transpose to correctly preserve the *TriangularBit.
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To try it with the unit tests set the cmake variable TEST_LIB to ON.
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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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which now supports selfadjoint matrix. The implementation follows
Golub's famous book.
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* added MatrixBase::real()
* added the ability to extract a selfadjoint matrix from the
lower or upper part of a matrix, e.g.:
m.extract<Upper|SelfAdjoint>()
will ignore the strict lower part and return a selfadjoint.
This is compatible with ZeroDiag and UnitDiag.
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-fix CMakeLists, public headers weren't getting installed
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(does not support complex and does not re-use the QR decomposition)
* Rewrite the cache friendly product to have only one instance per scalar type !
This significantly speeds up compilation time and reduces executable size.
The current drawback is that some trivial expressions might be
evaluated like conjugate or negate.
* Renamed "cache optimal" to "cache friendly"
* Added the ability to directly access matrix data of some expressions via:
- the stride()/_stride() methods
- DirectAccessBit flag (replace ReferencableBit)
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part of a matrix. Triangular also provide an optimised method for forward
and backward substitution. Further optimizations regarding assignments and
products might come later.
Updated determinant() to take into account triangular matrices.
Started the QR module with a QR decompostion algorithm.
Help needed to build a QR algorithm (eigen solver) based on it.
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Gael Guennebaud
Benoit Jacob