construction of generic expressions working
for both dense and sparse matrix. A nicer solution
would be to use CwiseBinaryOp for any kind of matrix.
To this end we either need to change the overall design
so that the base class(es) depends on the kind of matrix,
or we could add a template parameter to each expression
type (e.g., int Kind = ei_traits<MatrixType>::Kind)
allowing to specialize each expression for each kind of matrix.
* Extend AutoDiffScalar to work with sparse vector expression
for the derivatives.
- rename EvalBeforeAssignBit to MayAliasBit
- make .lazy() remove the MayAliasBit only, and mark it as deprecated
- add a NoAlias pseudo expression, and MatrixBase::noalias() function
Todo:
- we have to decide whether += and -= assume no aliasing by default ?
- once we agree on the API: update the Sparse module and the unit tests respectively.
* move solveTriangular*() to TriangularView::solve*()
* move .llt() to SelfAdjointView
* add a high level wrapper to the efficient selfadjoint * vector product
* improve LLT so that we can specify which triangular part is meaningless
=> there are still many things to do (doc, cleaning, improve the matrix products, etc.)
introduce ei_is_diagonal to check for it
DiagonalCoeffs ---> Diagonal and allow Index to by Dynamic
-> add MatrixBase::diagonal(int) with unittest and doc
Until now, the user had to edit the source code to do that.
Internally, add EIGEN_ALIGN that takes into account both EIGEN_DONT_ALIGN.and
EIGEN_ARCH_WANTS_ALIGNMENT. From now on, only EIGEN_ALIGN should be used to
test whether we want to align.
Pommier. They are for float only, and they return exactly the same
result as the standard versions in about 90% of the cases. Otherwise the max error
is below 1e-7. However, for very large values (>1e3) the accuracy of sin and cos
slighlty decrease. They are about 3 or 4 times faster than 4 calls to their respective
standard versions. So, is it ok to enable them by default in their respective functors ?
* add Homogeneous expression for vector and set of vectors (aka matrix)
=> the next step will be to overload operator*
* add homogeneous normalization (again for vector and set of vectors)
* add a Replicate expression (with uni-directional replication
facilities)
=> for all of them I'll add examples once we agree on the API
* fix gcc-4.4 warnings
* rename reverse.cpp array_reverse.cpp
* add an efficient selfadjoint * vector implementation (= blas symv)
perf are inbetween MKL and GOTO
=> the interface is still missing (have to be rethougth)
