Triangular class
- full meta-unrolling in Part
- move inverseProduct() to MatrixBase
- compilation fix in ProductWIP: introduce a meta-selector to only do
direct access on types that support it.
- phase out the old Product, remove the WIP_DIRTY stuff.
- misc renaming and fixes
- fallback to normal product for small dynamic matrices
- overloaded "c += (a * b).lazy()" to avoid the expensive and useless temporary and setZero()
in such very common cases.
* fix a couple of issues with the flags
* Introduce a new highly optimized matrix-matrix product for large
matrices. The code is still highly experimental and it is activated
only if you define EIGEN_WIP_PRODUCT at compile time.
Currently the third dimension of the product must be a factor of
the packet size (x4 for floats) and the right handed side matrix
must be column major.
Moreover, currently c = a*b; actually computes c += a*b !!
Therefore, the code is provided for experimentation purpose only !
These limitations will be fixed soon or later to become the default
product implementation.
extended cache optimal product to work in any row/column
major situations, and a few bugfixes (forgot to add the
Cholesky header, vectorization of CwiseBinary)
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.
Currently only the following platform/operations are supported:
- SSE2 compatible architecture
- compiler compatible with intel's SSE2 intrinsics
- float, double and int data types
- fixed size matrices with a storage major dimension multiple of 4 (or 2 for double)
- scalar-matrix product, component wise: +,-,*,min,max
- matrix-matrix product only if the left matrix is vectorizable and column major
or the right matrix is vectorizable and row major, e.g.:
a.transpose() * b is not vectorized with the default column major storage.
To use it you must define EIGEN_VECTORIZE and EIGEN_INTEL_PLATFORM.
in ei_xpr_copy and operator=, respectively.
* added Matrix::lazyAssign() when EvalBeforeAssigningBit must be skipped
(mainly internal use only)
* all expressions are now stored by const reference
* added Temporary xpr: .temporary() must be called on any temporary expression
not directly returned by a function (mainly internal use only)
* moved all functors in the Functors.h header
* added some preliminaries stuff for the explicit vectorization
* added "all" and "any" special redux operators
* added support bool matrices
* added support for cost model of STL functors via ei_functor_traits
(By default ei_functor_traits query the functor member Cost)
when to evaluate arguments and when to meta-unroll.
-use it in Product to determine when to eval args. not yet used
to determine when to unroll. for now, not used anywhere else but
that'll follow.
-fix badness of my last commit
to disable eigen's asserts without disabling one's own program's
asserts. Notice that Eigen code should now use ei_assert()
instead of assert().
* Remove findBiggestCoeff() as it's now almost redundant.
* Improve echelon.cpp: inner for loop replaced by xprs.
* remove useless "(*this)." here and there. I think they were
first introduced by automatic search&replace.
* fix compilation in Visitor.h (issue triggered by echelon.cpp)
* improve comment on swap().
as well as partial redux (vertical or horizontal redux).
Includes shortcuts for: sum, minCoeff and maxCoeff.
There is no shortcut for the partial redux.
* Added a generic *visitor* mini framework. A visitor is a custom object
sequentially applied on each coefficient with knowledge of its value and
coordinates.
It is currentlly used to implement minCoeff(int*,int*) and maxCoeff(int*,int*).
findBiggestCoeff is now a shortcut for "this->cwiseAbs().maxCoeff(i,j)"
* Added coeff-wise min and max.
* fixed an issue with ei_pow(int,int) and gcc < 4.3 or ICC
- add cwiseExp(), cwiseLog()...
--> for example, doing a gamma-correction on a bitmap image stored as
an array of floats is a simple matter of:
Eigen::Map<VectorXf> m = VectorXf::map(bitmap,size);
m = m.cwisePow(gamma);
- apidoc improvements, reorganization of the \name's
- remove obsolete examples
- remove EIGEN_ALWAYS_INLINE on lazyProduct(), it seems useless.
internal classes: AaBb -> ei_aa_bb
IntAtRunTimeIfDynamic -> ei_int_if_dynamic
unify UNROLLING_LIMIT (there was no reason to have operator= use
a higher limit)
etc...
Finally the importing macro is named EIGEN_BASIC_PUBLIC_INTERFACE
because it does not only import the ei_traits, it also makes the base class
a friend, etc.
template parameter "Scalar" is removed. This is achieved by introducting a
template <typename Derived> struct Scalar to achieve a forward-declaration of
the Scalar typedefs.
If the number of coefficients does not match the matrix size, then an assertion is raised.
No support for xpr on the right side for the moment.
* Added support for assertion checking. This allows to test that an assertion is indeed raised
when it should be.
* Fixed a mistake in the CwiseUnary example.
- compatible with current STL's functors as well as with the extention proposal (TR1)
* thanks to the above, Cast and ScalarMultiple have been removed
* benchmark_suite is more flexible (compiler and matrix size)
* functor templates are not template template parameter anymore
(this allows to make templated functors !)
* Main page: extented compiler discussion
* A small hack to support gcc 3.4 and 4.0 (see the main page)
* Fix a cast type issue in Cast
* Various doxygen updates (mainly Cwise stuff and added doxygen groups
in MatrixBase to split the huge memeber list, still not perfect though)
* Updated Gael's email address
