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Eigen is a header-only C++ library that you can use to
create both dynamic and static matrices, vectors, and the like.
Eigen was inspired by the work of Jeff Heaton and the original BLAS and LAPACK libraries (BTL and ATL).
Eigen is well suited for linear algebra, the domain for BLAS and LAPACK. Eigen also works perfectly with other
C++ libraries that use linear algebra (i.e. Boost, Numerical C++, SONM, and so on).
The Eigen library is free of charge and is open-source (LGPL v2.1 or later).
History
Eigen is written by Fabian Giesen, Eike Rathke, and the Eigen mailing list. The previous version was written by Chris
Anderson.
Eigen was originally implemented with support for full-precision, double-precision, and complex numbers and was optimized
for standard-conforming C++ compilers. The implementation was later extended to support single-precision floating point
numbers, matrix-matrix multiplication, and extended matrix classes.
Features
– Built-in support for both dynamic and static matrices and vectors
– C++98/C++11/C++14
– Thousands of methods that you can use to manipulate your matrices
– Custom matrix classes (e.g. sparse, eigen-trivial, etc.)
– Eigen is header-only, so you don’t need to include any C++ header
– The library is optimized for C++ compilers, so it should compile on all common compilers
– Eigen is free of charge and open-source, so you can redistribute it as you wish
– You can use it with Boost, Numerical C++, and SONM
– And you can use Eigen to create better and more powerful C++ libraries
Why Eigen?
The current version of Eigen is written in C++ and provides a clean interface that is easy to understand.
The algorithms of Eigen are more or less the same as the original LAPACK and BLAS, but it is implemented with C++.
Eigen uses templates to provide support for arbitrary matrix sizes, including static matrices, and has almost the
same amount of functions as LAPACK. Eigen also supports N-dimensional vectors and is more flexible than the original
BLAS and L 4f8c9c8613

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