ALGLIB
Today, we want to delve into the fascinating world of ALGLIB. Knowing more about ALGLIB will allow us to explore its different facets and understand its importance in various contexts. Throughout this article, we will explore different aspects related to ALGLIB, from its origin to its evolution over time. In addition, we will analyze its impact on modern society and its relevance in today's world. Through a comprehensive and detailed approach, we hope to provide our readers with a comprehensive and enriching insight into ALGLIB.
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| Original author(s) | Bochkanov Sergey Anatolyevich |
|---|---|
| Developer(s) | ALGLIB LTD (UK) |
| Stable release | 4.03
/ 26 September 2024 |
| Operating system | Cross-platform |
| Type | Numerical library |
| License | Dual (commercial, GPL) |
| Website | www |
ALGLIB is a cross-platform open source numerical analysis and data processing library. It can be used from several programming languages (C++, C#, VB.NET, Python, Delphi, Java).
ALGLIB started in 1999 and has a long history of steady development with roughly 1-3 releases per year. It is used by several open-source projects, commercial libraries, and applications (e.g. TOL project, Math.NET Numerics,[1][2] SpaceClaim[3]).
Features
Distinctive features of the library are:
- Support for several programming languages with identical APIs (as of 2023, it supports C++, C#, FreePascal/Delphi, VB.NET, Python, and Java)
- Self-contained code with no mandatory external dependencies and easy installation
- Portability (it was tested under x86/x86-64/ARM, Windows and Linux)
- Two independent backends (pure C# implementation, native C implementation) with automatically generated APIs (C++, C#, ...)
- Same functionality of commercial and GPL versions, with enhancements for speed and parallelism provided in the commercial version
The most actively developed parts of ALGLIB are:
- Linear algebra, offering a comprehensive set of both dense and sparse linear solvers and factorizations
- Interpolation, featuring standard algorithms like polynomials and 1D/2D splines, as well as several unique large-scale interpolation/fitting algorithms. These include penalized 1D/2D splines, fast thin plate splines and fast polyharmonic splines, all scalable to hundreds of thousands of points.
- Least squares solvers, including linear/nonlinear unconstrained and constrained least squares and curve fitting solvers
- Optimization, with LP, QP, QCQP, SOCP (and other conic problem types) and NLP solvers, derivative-free global solvers and multiobjective optimization algorithms.
- Data analysis, with various algorithms being implemented
The other functions in the library include:
- Fast Fourier transforms
- Numerical integration
- Ordinary differential equations
- Special functions
- Statistics (descriptive statistics, hypothesis testing)
- Multiple precision versions of linear algebra, interpolation and optimization algorithms (using MPFR for floating point computations)
See also
References
- ^ "Math.NET Numerics". Numerics.mathdotnet.com. Retrieved 2010-07-10.
- ^ "Math.NET Numerics Contributors". GitHub.com. Retrieved 2013-05-07.
- ^ "End User License". .spaceclaim.com. Retrieved 2010-07-10.