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* 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.)
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it never made very precise sense. but now does it still make any?
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Added default ctor and public compute method as
well as safe-guards against uninitialized usage.
Added unit tests for the safe-guards.
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mark isFullRank() deprecated,
add solve() (mix of Keir's patch and LU::solve())
=> there is big problem with complex which are not working
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* computation
* Array: add a count() method and rename AllAndAny.h file to
BooleanRedux.h
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for the comparison to 0)
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* finally get ei_add_test right
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Derived to MatrixBase.
* the optimization of eval() for Matrix now consists in a partial
specialization of ei_eval, which returns a reference type for Matrix.
No overriding of eval() in Matrix anymore. Consequence: careful,
ei_eval is no longer guaranteed to give a plain matrix type!
For that, use ei_plain_matrix_type, or the PlainMatrixType typedef.
* so lots of changes to adapt to that everywhere. Hope this doesn't
break (too much) MSVC compilation.
* add code examples for the new image() stuff.
* lower a bit the precision for floats in the unit tests as
we were already doing some workarounds in inverse.cpp and we got some
failed tests.
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* fix some "unused variable" warnings in the tests; there remains a libstdc++ "deprecated"
warning which I haven't looked much into
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of the stable API yet and a couple of other minor doc updates...
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pivoting for better numerical stability. For now the only application is
determinant.
* New determinant unit-test.
* Disable most of Swap.h for now as it makes LU fail (mysterious).
Anyway Swap needs a big overhaul as proposed on IRC.
* Remnants of old class Inverse removed.
* Some warnings fixed.
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- added explicit enum to int conversion where needed
- if a function is not defined as declared and the return type is "tricky"
then the type must be typedefined somewhere. A "tricky return type" can be:
* a template class with a default parameter which depends on another template parameter
* a nested template class, or type of a nested template class
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Documentation:
* add an overview for each module.
* add an example for .all() and Cwise::operator<
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Renamed "MatrixBase::extract() const" to "MatrixBase::part() const"
* Renamed static functions identity, zero, ones, random with an upper case
first letter: Identity, Zero, Ones and Random.
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- fix compilation in product.cpp with std::complex
- fix bug in MatrixBase::operator!=
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To try it with the unit tests set the cmake variable TEST_LIB to ON.
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- works for complex
- allows direct access to the matrix R
* removed the scale by the matrix dimensions in MatrixBase::isMuchSmallerThan(scalar)
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* added qr and eigensolver tests
* fix a compilation warning in Matrix copy constructor
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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
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(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)
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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.
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Gael Guennebaud
Benoit Jacob
Hauke Heibel