Renamed meta_{true|false} to {true|false}_type, meta_if to conditional, is_same_type to is_same, un{ref|pointer|const} to remove_{reference|pointer|const} and makeconst to add_const.
Changed boolean type 'ret' member to 'value'.
Changed 'ret' members refering to types to 'type'.
Adapted all code occurences.
* Clean a bit the Triadiagonalization making sure it the inplace
function really works inplace ;), and that only the lower
triangular part of the matrix is referenced.
* Remove the Tridiagonalization member object of SelfAdjointEigenSolver
exploiting the in place capability of HouseholdeSequence.
* Update unit test to check SelfAdjointEigenSolver only consider
the lower triangular part.
* use SelfAdjointView instead of Eigen2's SelfAdjoint flag.
* add tests and documentation.
* allow eigenvalues() for non-selfadjoint matrices.
* they no longer depend only on SelfAdjointEigenSolver, so move them to
a separate file
This changes the return type of:
* eigenvectors() and eigenvalues() in ComplexEigenSolver
* eigenvalues() in EigenSolver
* eigenvectors() and eigenvalues() in SelfAdjointEigenSolver
This is to avoid dynamic memory allocations in the compute() methods of
ComplexEigenSolver, EigenSolver, and SelfAdjointEigenSolver where possible.
As a result, Tridiagonalization::decomposeInPlace() is no longer used.
Biggest remaining issue is the allocation in HouseholderSequence::evalTo().
* reduce scope of declarations
* use that low = 0 and high = size-1
* rename some variables
* rename hqr2_step2() to computeEigenvectors()
* exploit that ei_isMuchSmallerThan takes absolute value of arguments
* get rid of BlockReturnType: it was not needed, and code was not always using it consistently anyway
* add topRows(), leftCols(), bottomRows(), rightCols()
* add corners unit-test covering all of that
* adapt docs, expand "porting from eigen 2 to 3"
* adapt Eigen2Support
- Updated unit tests to check above constructor.
- In the compute() method of decompositions: Made temporary matrices/vectors class members to avoid heap allocations during compute() (when dynamic matrices are used, of course).
These changes can speed up decomposition computation time when a solver instance is used to solve multiple same-sized problems. An added benefit is that the compute() method can now be invoked in contexts were heap allocations are forbidden, such as in real-time control loops.
CAVEAT: Not all of the decompositions in the Eigenvalues module have a heap-allocation-free compute() method. A future patch may address this issue, but some required API changes need to be incorporated first.
