File too long (should be no more than 24 lines).
Line too long (should be no more than 80 characters).
Trailing empty lines.
Trailing white-space.
Trucated the long files as best as possible while preserving the most info
contained in them.
Thiss fixes a compilation issue with aligned_allocator, and a typo
in the ParametrizedLine documentation.
2.0.16:
Fix bug in 3x3 tridiagonlisation (and consequently in 3x3 selfadjoint eigen decomposition).
Fix compilation for new gcc 4.6.
Fix performance regression since 2.0.12: in some matrix-vector product, complex matrix expressions were not pre-evaluated.
Fix documentation of Least-Square.
New feature: support for part<SelfAdjoint>.
Fix bug in SparseLU::setOrderingMethod.
This changes the buildlink3.mk files to use an include guard for the
recursive include. The use of BUILDLINK_DEPTH, BUILDLINK_DEPENDS,
BUILDLINK_PACKAGES and BUILDLINK_ORDER is handled by a single new
variable BUILDLINK_TREE. Each buildlink3.mk file adds a pair of
enter/exit marker, which can be used to reconstruct the tree and
to determine first level includes. Avoiding := for large variables
(BUILDLINK_ORDER) speeds up parse time as += has linear complexity.
The include guard reduces system time by avoiding reading files over and
over again. For complex packages this reduces both %user and %sys time to
half of the former time.
Eigen 2 is a C++ template library for linear algebra: vectors, matrices, and
related algorithms. It is:
* Versatile. Eigen handles, without code duplication, and in a completely
integrated way:
o both fixed-size and dynamic-size matrices and vectors.
o both dense and sparse (the latter is still experimental) matrices and
vectors.
o both plain matrices/vectors and abstract expressions.
o both column-major (the default) and row-major matrix storage.
o both basic matrix/vector manipulation and many more advanced, specialized
modules providing algorithms for linear algebra, geometry, quaternions,
or advanced array manipulation.
* Fast.
o Expression templates allow to intelligently remove temporaries and enable
lazy evaluation, when that is appropriate -- Eigen takes care of this
automatically and handles aliasing too in most cases.
o Explicit vectorization is performed for the SSE (2 and later) and AltiVec
instruction sets, with graceful fallback to non-vectorized code.
Expression templates allow to perform these optimizations globally for
whole expressions.
o With fixed-size objects, dynamic memory allocation is avoided, and the
loops are unrolled when that makes sense.
o For large matrices, special attention is paid to cache-friendliness.
* Elegant. The API is extremely clean and expressive, thanks to expression
templates. Implementing an algorithm on top of Eigen feels like just copying
pseudocode. You can use complex expressions and still rely on Eigen to
produce optimized code: there is no need for you to manually decompose
expressions into small steps.
* Compiler-friendy. Eigen has very reasonable compilation times at least with
GCC, compared to other C++ libraries based on expression templates and heavy
metaprogramming. Eigen is also standard C++ and supports various compilers.
