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SciPy (pronounced / ˈ s aɪ p aɪ / "sigh pie" [2]) is a free and open-source Python library used for scientific computing and technical computing. [3]SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
A numerical modeling environment with a declarative and visual programming language based on influence diagrams. Ch: SoftIntegration 1 October 2001: 7.5.1 2 December 2015: $399 (commercial), $199 (academic), Free (student) Proprietary: C/C++ based numerical computing and graphical plotting [1] DADiSP: DSP Development 1984 1987 6.7 B02 17 ...
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. [2]
High-performance C++ linear algebra library based on Generic programming: NAG Numerical Library: The Numerical Algorithms Group: C, Fortran 1971 many components Non-free Proprietary General purpose numerical analysis library. NMath: CenterSpace Software: C# 2003 7.1 / 12.2019 Non-free Proprietary Math and statistical libraries for the .NET ...
Enthought, Inc. is a software company based in Austin, Texas, United States that develops scientific and analytic computing solutions using primarily the Python programming language. It is best known for the early development and maintenance of the SciPy library of mathematics, science, and engineering algorithms [ 3 ] and for its Python for ...
The SciPy scientific library, for instance, uses HiGHS as its LP solver [13] from release 1.6.0 [14] and the HiGHS MIP solver for discrete optimization from release 1.9.0. [15] As well as offering an interface to HiGHS, the JuMP modelling language for Julia [16] also describes the specific use of HiGHS in its user documentation. [17]
Variants of this algorithm are available in MATLAB as the routine lsqnonneg [8] [1] and in SciPy as optimize.nnls. [9] Many improved algorithms have been suggested since 1974. [1] Fast NNLS (FNNLS) is an optimized version of the Lawson–Hanson algorithm. [2]