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All checksums have been double-checked against existing RMD160 and
SHA512 hashes
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Fixes PR pkg/54108: misc/step fails build under -current 8.99.37
bump PKGREVISION.
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Problems found locating distfiles:
Package dfftpack: missing distfile dfftpack-20001209.tar.gz
Package eispack: missing distfile eispack-20001130.tar.gz
Package fftpack: missing distfile fftpack-20001130.tar.gz
Package linpack: missing distfile linpack-20010510.tar.gz
Package minpack: missing distfile minpack-20001130.tar.gz
Package odepack: missing distfile odepack-20001130.tar.gz
Package py-networkx: missing distfile networkx-1.10.tar.gz
Package py-sympy: missing distfile sympy-0.7.6.1.tar.gz
Package quadpack: missing distfile quadpack-20001130.tar.gz
Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden). All existing
SHA1 digests retained for now as an audit trail.
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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.
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A years worth of fixes since 2.0.4.
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Changes unknown.
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Release version. Cleans up alignment issues.
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changes unspecified - better handling of systems without posix_memalign.
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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.
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