* * *
License disclaimer changed to BSD license for MKL_support.h
* * *
Pardiso support fixed, test added.
blas/lapack tests fixed: Scalar parameter was added in Cholesky, product_matrix_vector_triangular remaned to triangular_matrix_vector_product.
* * *
PARDISO test was added physically.
* 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.
* Introduction of strides-at-compile-time so for example the optimized code really knows when it needs to evaluate to a temporary
* StorageKind / XprKind
* Quaternion::setFromTwoVectors: use JacobiSVD instead of SVD
* ComplexSchur: support the 1x1 case
NOTE: The ComplexEigenSolver class currently _does_ allocate (line 135 of Eigenvalues/ComplexEigenSolver.h), but the reason appears to be in the implementation of matrix-matrix products, and not in the decomposition itself.
The nomalloc unit test has been extended to verify that decompositions do not allocate when max sizes are specified. There are currently two workarounds to prevent the test from failing (see comments in test/nomalloc.cpp), both of which are related to matrix products that allocate on the stack.
* be aware of number of actual householder vectors
(optimization in non-full-rank case, no behavior change)
* fix applyThisOnTheRight, it was using k instead of actual_k
* QR: rename matrixQ() to householderQ() where applicable
* with random matrices multiplied by 1e+8 (i.e. fixed wrong absolute fuzzy compare)
* with 10,000 repetitions (i.e. the fuzzy compare is really clever)
and when it occasionnally fails, less than once in 10,000 repeats, it is only on the exact rank computation.
