Introduction of DenseCoeffBase (revision bfdc1c4973
) meant that non-const
operator() is only defined if DirectAccess is set. This caused the line
"m.reverse()(1,0) = 4;" in MatrixBase_reverse.cpp to fail at compile-time.
Not sure this is correct solution; perhaps we should disallow this? Or make
Reverse DirectAccess with a negative stride - would that break something?
* 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
Rationale: coeffRef() methods should only exist when we have DirectAccess. So a natural thing to do would have been to use enable_if, but since there are many methods it made more sense to do the "enable_if" for the whole group by introducing a new class. And that also that the benefit of not changing method prototypes.
I didn't even put it in Eigen2Support because it requires several other modules. But if you want we can always create a new module, Eigen2Support_LeastSquares...
* Now completely generic so all standard integer types (like char...) are supported.
** add unit test for that (integer_types).
* NumTraits does no longer inherit numeric_limits
* All math functions are now templated
* Better guard (static asserts) against using certain math functions on integer types.
* 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.
* adapt Eigenvalues module to the new rule that the RowMajorBit must have the proper value for vectors
* Fix RowMajorBit in ei_traits<ProductBase>
* Fix vectorizability logic in CoeffBasedProduct
* 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
of ei_matrix_array for size 0
* adapt many xprs to have the right storage order, now that it matters
* add static assert on expressions to check that vector xprs
have the righ storage order
* adapt ei_plain_matrix_type_(column|row)_major
* implement assignment of selfadjointview to matrix
(was before failing to compile) and add nestedExpression() methods
* expand product_symm test
* in ei_gemv_selector, use the PlainObject type instead of a custom Matrix<...> type
* fix VectorBlock and Block mistakes
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.
* kill EIGEN_DONT_ALIGN_HEAP option (one should use EIGEN_DONT_ALIGN)
* rename EIGEN_DONT_ALIGN_STACK to EIGEN_DONT_ALIGN_STATICALLY. hope it's a better name.
as gcc on ARM (both CodeSourcery 4.4.1 used and experimental 4.5) fail to
ensure proper alignment with __attribute__((aligned(16))). This has to be
fixed upstream to remove the workarounds.
* use them (big simplification in Assign.h)
* axe (Inner|Outer)StrideAtCompileTime that were just introduced
* ei_int_if_dynamic now asserts that the size is the expected one: adapt to that in Block.h
* add rowStride() / colStride() in DenseBase
* implement innerStride() / outerStride() everywhere needed
Finally the createRandomMatrixOfRank() function is renamed to createRandomPIMatrixOfRank, where PI stands for 'partial isometry', that is, a matrix whose singular values are 0 or 1.
(fixes lu test failures when testing solve())
* LU test: set appropriate threshold and limit the number of times that a specially tricky test
is run. (fixes lu test failures when testing rank()).
* Tests: rename createRandomMatrixOfRank to createRandomProjectionOfRank
Added setLinSpaced/LinSpaced functionality to DenseBase.
Improved vectorized assignment - overcomes MSVC optimization issues.
CwiseNullaryOp is now requiring functors to offer 1D and 2D operators.
Adapted existing functors to the new CwiseNullaryOp requirements.
Added ei_plset to create packages as [a, a+1, ..., a+size].
Added more nullaray unit tests.
Necessary to get the test to compile after c5d7c9f0de
.
I'm assuming that isUpperTriangular() is the name we want; the alternative
is to change Eigen/src/Core/{MatrixBase,TriangularMatrix}.h
sizeof(Scalar), and that assumption breaks with double on linux x86-32.
* Rename ei_alignmentOffset to ei_first_aligned
* Rewrite its documentation and part of its body
* The variant taking a MatrixBase doesn't need a separate size argument.
significantly simplify the code of these checks while extending them
to catch much more expressions!
* move the enabling/disabling of vectorized sin/cos to the architecture traits
division instead of RCPPS-followed-by-Newton-Raphson. The rationale for that is
that elsewhere in Eigen we dont allow ourselves this approximation (which throws
2 bits of mantissa), so there's no reason we should allow it here.
* inverse tests: use createRandomMatrixOfRank, use more strict precision
* tests: createRandomMatrixOfRank: support 1x1 matrices
* determinant: nest the xpr
* Minor: add comment
* 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
