was a sign that we were doing something wrong. In fact, having
NestByValue as a special case of Flagged was wrong, and the previous
commit, while not buggy, was inefficient because then when the resulting
NestByValue xpr was nested -- hence copied -- the original xpr which was
already nested by value was copied again; hence instead of 1 copy we got
3 copies.
The solution was to ressuscitate the old Temporary.h (renamed
NestByValue.h) as it was the right approach.
finally that's more subtle than just using ei_nested, because when
flagging with NestByValueBit we want to store the expression by value
already, regardless of whether it already had the NestByValueBit set.
* rename temporary() ----> nestByValue()
* move the old Product.h to disabled/, replace by what was ProductWIP.h
* tweak -O and -g flags for tests and examples
* reorder the tests -- basic things go first
* simplifications, e.g. in many methoeds return derived() and count on
implicit casting to the actual return type.
* strip some not-really-useful stuff from the heaviest tests
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
(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)
* Fix compilation of Inverse.h with vectorisation
* Introduce EIGEN_GNUC_AT_LEAST(x,y) macro doing future-proof (e.g. gcc v5.0) check
* Only use ProductWIP if vectorisation is enabled
* rename EIGEN_ALWAYS_INLINE -> EIGEN_INLINE with fall-back to inline keyword
* some cleanup/indentation
(needed by the new product implementation)
* Make the packet* members template to support aligned and unaligned
access. This makes Block vectorizable. Combined with ReferencableBit,
we should be able to determine at runtime (in some specific cases) if
an aligned vectorization is possible or not.
* Improved the new product implementation to robustly handle all cases,
it now passes all the tests.
* Renamed the packet version ei_predux to ei_preduxp to avoid name collision.
* 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.
m.upper() = a+b;
only updates the upper triangular part of m.
Note that:
m = (a+b).upper();
updates all coefficients of m (but half of the additions
will be skiped)
Updated back/forward substitution to better use Eigen's capability.
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.
- support dynamic sizes
- support arbitrary matrix size when the matrix can be seen as a 1D array
(except for fixed size matrices where the size in Bytes must be a factor of 16,
this is to allow compact storage of a vector of matrices)
Note that the explict vectorization is still experimental and far to be completely tested.
are provided to handle not suported types seemlessly.
Added a generic null-ary expression with null-ary functors. They replace
Zero, Ones, Identity and Random.
* add -pedantic to CXXFLAGS
* cleanup intricated expressions with && and ||
which gave warnings because of "missing" parentheses
* fix compile error in NumTraits, apparently discovered
by -pedantic
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
to preserve SVN history). They are made useless by the new
ei_eval_unless_lazy.
- introduce a generic Eval member typedef so one can do e.g.
T t; U u; Product<T, U>::Eval m; m = t*u;
* macro renaming: EIGEN_NDEBUG becomes EIGEN_NO_DEBUG
as this is much better (and similar to Qt) and
EIGEN_CUSTOM_ASSERT becomes EIGEN_USE_CUSTOM_ASSERT
* protect Core header by a EIGEN_CORE_H
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
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
Rework the matrix storage to ensure optimal sizeof in all cases, while
keeping the decoupling of matrix sizes versus storage sizes.
Also fixing (recently introduced) bugs caused by unwanted
reallocations of the buffers.
- finally get the Eval stuff right. get back to having Eval as
a subclass of Matrix with limited functionality, and then,
add a typedef MatrixType to get the actual matrix type.
- add swap(), findBiggestCoeff()
- bugfix by Ramon in Transpose
- new demo: doc/echelon.cpp
