161 packages tagged with “algebra”
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac.
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac. This package contains strong-named assemblies for legacy use cases (not recommended).
F# Modules for Math.NET Numerics, the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac.
Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for constrained and unconstrained problems, special functions and other tools for scientific applications. This package is part of the Accord.NET Framework.
A small library for performing matrix math, linear algebra - now including sparse matrix solve. Most functions are static and use simple arrays (e.g double[,]) making it easy to use in other projects.
Intel oneAPI MKL native libraries for Math.NET Numerics on Windows.
Math.NET Symbolics is a basic open source computer algebra library for .Net and Mono. Written in F# but works well in C# as well. Supports .Net Framework 4.5 or higher and .Net Standard 2.0 or higher, on Windows, Linux and Mac.
Numerics.NET (formerly Extreme Optimization Numerical Libraries for .NET) are a set of libraries for numerical computing and data analysis. This is the main package that contains all the core functionality. For optimal performance, we strongly recommend also referencing one of the native packages based on Intel's Math Kernel Library (MKL). Supports .NET 5.0-8.0+, .NET Framework 4.62+, .NET Standard 2.0, and .NET Core 3.1 on Windows, Linux and Mac.
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires Visual Studio 2015-2019 C++ x64 runtime. Requires x64 hardware.
A simple cross platform .NET API for Intel MKL. Reference the MKL.NET package and required runtime packages and use the static MKL functions. The correct native libraries will be included and loaded at runtime. Exposing functions from MKL keeping the syntax as close to the c developer reference as possible: https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top.html
Lightweight optimizer of System.Linq.Expression expressions. Just basic boolean algebra and reductions, constant and tuple/anonymous type eliminations. For side-effect free Expressions. No compilation-subjective optimizations.
Enables to work with formulas built in the code or from a string. Computing, derivating, latex rendering, compilation, solving equations and systems of equations analytycally, simplification, and much more. Read more on https://am.angouri.org.
C# bindings for NumPy - a fundamental library for scientific computing, machine learning and AI. Does not require a local Python installation!
The GPU-accelerated version of package CenterSpace.NMath. With a few minor exceptions, such as optional GPU configuration settings, the API is identical between CenterSpace.NMath.Premium and CenterSpace.NMath. If using at least .NET Framework 4.6.1 or .NET Core 2.0, we recommend using one of our NMath .NET Standard NuGet packages.
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires libgcc linux. Requires x64 hardware.
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. If you're using at least .NET Framework 4.6.1 or .NET Core 2.0, we recommend using one of our NMath .NET Standard NuGet packages.
C# bindings for NumPy on Win64 - a fundamental library for scientific computing, machine learning and AI. Does require Python 3.7 with NumPy 1.16 installed!
OpenBLAS native libraries for Math.NET Numerics.
.NET library providing reusable math classes.
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5 or .NET Core 2.0. Does not support .NET Framework. Requires Visual Studio 2017 C++ x86 and x64 runtimes. Requires x64 hardware.
Cross-platform .NET library for digital signal processing. Fast and optimized.
Performance and memory optimised matrix algebra library based on cross platform MKL.NET. - Matrix expressions are optimised to perform intermediate calculations inplace and reuse memory. - Operations such as scale, transpose, +, * are combined into single MKL calls. - Intermediate matrices are disposed (or reused) automatically. - ArrayPool underlying memory model using IDisposable and Finalizers. - Uses the Pinned Object Heap for net6.0.
Bright Wire is an open source machine learning library. Includes neural networks (feed forward, convolutional and recurrent), naive bayes, linear regression, decision trees, logistic regression, k-means clustering and dimensionality reduction.
C#/.NET math library for small vectors and matrices. Supported types: vec2, vec3, vec4, quat, mat2, mat3, mat4, non-quadratic mats. Supported base types: int, uint, long, float, double, decimal, complex, long, bool, generic T. Supports swizzling, operator overloads, numerous functions. Supports serialization and marshalling. The naming and behavior is inspired by the excellent OpenGL Mathematics lib by Christophe Riccio. This library is MIT-licensed.
F# Modules for Math.NET Numerics, the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac. This package contains strong-named assemblies for legacy use cases (not recommended).
Function programming goodness: algebraic structures, Maybe, Either, Unit, State, Writer, Functor, Monad, Monoid, Lenses, and more.