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uBLAS is a C++ template class library that provides BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices. Dlib: Davis E. King C++ 2006 19.24.2 / 05.2023 Free Boost C++ template library; binds to optimized BLAS such as the Intel MKL; Includes matrix decompositions, non-linear solvers, and machine learning tooling Eigen: Benoît ...
ALGLIB is an open source / commercial numerical analysis library with C++ version; Armadillo is a C++ linear algebra library (matrix and vector maths), aiming towards a good balance between speed and ease of use. [1] It employs template classes, and has optional links to BLAS and LAPACK. The syntax is similar to MATLAB.
An interface to the Python language is available through the PyArmadillo package, [4] which facilitates prototyping of algorithms in Python followed by relatively straightforward conversion to C++. Armadillo is a core dependency of the mlpack machine learning library [5] and the ensmallen C++ library for numerical optimization. [6]
Lis (Library of Iterative Solvers for linear systems; pronounced lis]) is a scalable parallel software library to solve discretized linear equations and eigenvalue problems that mainly arise from the numerical solution of partial differential equations using iterative methods.
[1] [2] [3] It comes with its own programming language, in which numerical algorithms can be implemented. GNU MCSim a simulation and numerical integration package, with fast Monte Carlo and Markov chain Monte Carlo capabilities. ML.NET is a free-software machine-learning library for the C# programming language. [4] [5]
The Matrix Template Library (MTL) is a linear algebra library for C++ programs. The MTL uses template programming , which considerably reduces the code length. All matrices and vectors are available in all classical numerical formats: float , double , complex<float> or complex<double> .
The legislation would also include $2.5 trillion in cuts to net mandatory spending, aimed at satisfying conservative members who oppose increasing the debt ceiling without accompanying cuts.
LAPACK, in contrast, was designed to effectively exploit the caches on modern cache-based architectures and the instruction-level parallelism of modern superscalar processors, [2]: "Factors that Affect Performance" and thus can run orders of magnitude faster than LINPACK on such machines, given a well-tuned BLAS implementation. [2]: "