now we also align to 8byte boundary fixed-size objects that are multiple of 8 bytes.
That's only useful for now for double, not e.g. for Vector2f, but that didn't seem to hurt. Am I missing something? Do you prefer that we don't align Vector2f at all?
Also, improvements in test_unalignedassert.
---> question: do we change the prefix eigen2/ to eigen3/ now?
no, better wait until we've also changed the repository name
* more message improvements: "Install Eigen" was unclear as it left
out other things like the BLAS library
-- simpplify by removing the 2nd template parameter
-- rename Functor to Derived, as now it's a usual CRTP
* Homogeneous:
-- in products, honor the Max sizes etc.
that eigen2 owes all its performance to nonstandard restrict keyword.
well, this can also improve portability in case some compiler doesn't have __restrict.
- R-SVD preconditioning now done with meta selectors to avoid compiling useless code
- SVD options now honored, with options to hint "at least as many rows as cols" etc...
- fix compilation in bad cases (rectangular and fixed-size)
- the check for termination is now done on the fly, no more goto (should have done that earlier!)
- support complex numbers
- big rewrite of the 2x2 kernel, much more robust
* Jacobi:
- fix weirdness in initial design, e.g. applyJacobiOnTheRight actually did the inverse transformation
- fully support complex numbers
- fix logic to decide whether to vectorize
- remove several clumsy methods
fix for complex numbers
- 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.
- all specialized products now inherits ProductBase
- the default product evaluated by Assign is still here,
but it is currently enabled for small fixed sizes only
- => this significantly speed up compilation for large matrices
- I left the OuterProduct specialization empty as an exercise...
=> faster a * b.transpose() product
=> this also fix a bug in a so far untested situation
* SYMM is now ready for use => still have to write the high level
stuff to convert natural expressions into a call to SYMM
* this allows to optimize xpr like C -= lazy_product, still have to catch "scalar_product_of_lazy_product"
* started to support conjugate in cache friendly products (very useful to evaluate A * B.adjoint() without
evaluating B.adjoint() into a temporary
* compilation fix
