DiagonalCoeffs. The current API is simply:
m.diagonal<1>() => 1st super diagonal
m.diagonal<-2>() => the 2nd sub diagonal
I'll add a code snippet once we agree on this API.
This should remove most portability issues to other platforms where data alignment issues (including
overloading operator new and new[]) can be tricky, and where data alignment is not needed in the first place.
The unsupported module documentation is automatically generated in:
build/doc/unsupported/
with bidirectional cross references.
I leave a class Foo in AdolcForward module to illustrate the
cross-reference behavior. I will remove it in the next commit.
* previous DiagonalMatrix expression is now DiagonalMatrixWrapper
* DiagonalMatrix class is now for storage
* add the DiagonalMatrixBase class to factorize code of the
two previous classes
* remove Scaling class (it is now a global function)
* add UniformScaling helper class
(don't use it directly, use the Scaling function)
* add the Scaling global function to simplify the creation
of scaling objects
There is still a lot to do, in particular about DiagonalProduct for which
the goal is to get rid of the "if()" in the coeff() function. At least
it is not worse than before ! Also need to uptade the tutorial and add more doc.
EIGEN_WORK_AROUND_QT_BUG_CALLING_WRONG_OPERATOR_NEW_FIXED_IN_QT_4_5
is the right way to go for allowing placement new on a class having
overloaded operator new. Qt 4.5 won't add the :: prefix. (I still do not
understand how you can distinghish a placement new from an overloaded
operator new taking an allocator as argument...)
Question 1: why are *=scalar and /=scalar working right away ?
Same weirdness in DynamicSparseMatrix where operators += and -= work wihout
having to redefine them ???
* try to be clever in matrix ctors and operator=: be lazy when we can, always allow
to copy rowvector into columnvector, check the template parameters,
try to factor the code better
* add missing copy ctor in UnalignedType
* fix bug in the traits of DiagonalProduct
* renaming: EIGEN_TUNE_FOR_CPU_CACHE_SIZE
* update the dox a little
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
