search: sparse-matrix

pkg

StarMath

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.

v2.0.20.204

↙ 881.3K / total

matrixarraymathlinearalgebra

pkg

CSparse

CSparse.NET provides numerical methods for sparse LU, Cholesky and QR decomposition of real and complex linear systems.

v4.3.0

↙ 137.3K / total

mathsparsematrixlucholesky

SoftCircuits.SparseCollections✓

The SparseCollections library provides the SparseArray<T> and SparseMatrix<T> collection classes. The array class allow statements such as "array[1000000] = 5" or "array[-1000000] = 6" without having to create a large array. The matrix class does the same thing using a two-dimensional-array metaphor.

v2.2.0

↙ 8.0K / total

sparse-arraysparse-matrixsparsearraymatrix

pkg

Mendz.Matrix

Provides a library of APIs for working with dense, (DOK) sparse and compressed (CRS, CCS, CVS) matrices.

v1.2.1

↙ 40.0K / total

matrixsparse-matrixcompressed-matrixcrsccs

CenterSpace.NMath.Standard.Windows.X64

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.

v7.4.2.16

↙ 954.0K / total

mathstatisticsnumericsprobabilitylinear

CenterSpace.NMath.Standard.Linux.X64

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.

v7.4.2.16

↙ 333.6K / total

mathstatisticsnumericsprobabilitylinear

CenterSpace.NMath

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.

v6.2.0.58

↙ 172.2K / total

mathstatisticsnumericslinearalgebra

pkg

cs-matrix

Linear Algebra implementation with Sparse Vector and Sparse Matrix

v1.0.1

↙ 1.9K / total

Accord.IO

Contains File and data parsers to load and save data from different file formats. This package is part of the Accord.NET Framework.

v3.8.0

↙ 176.1K / total

accord.netaforge.netframeworkstatisticsmnist

Bluebit.MatrixLibrary.x64

==================================== Bluebit .NET Matrix Library - 64 bit ==================================== This is a free version of .NET Matrix Library (NML™) which will allow matrix sizes up to 1000 x 1000. The Bluebit .NET Matrix Library (NML™) provides classes for object-oriented linear algebra in the .NET platform. It can be used to solve systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalues and eigenvectors problems, and singular value problems. Also provided are the associated matrix factorizations such as Eigen, LQ, LU, Cholesky, QR, SVD. .NET Matrix Library (NML™) also supports sparse matrices and advanced methods for solving large sparse systems of linear equations. The above functionality is present for both real and complex matrices. Two analogous sets of classes are provided for real and complex matrices, vectors and factorizations. While exposing an easy to use and powerful interface, the Bluebit .NET Matrix Library does not sacrifice any performance. Highly optimized BLAS and the standard LAPACK routines are used within the library and provide fast execution and accurate calculations. The Bluebit .NET Matrix Library has been developed as a mixed mode C++ project, combining together managed and unmanaged code and delivering the best of both worlds; the speed of native C++ code and the feature-rich and easy to use environment of the .NET Framework.

v7.0.1

↙ 4.5K / total

matrixmathlinearalgebraeigenvalues

Bluebit.MatrixLibrary.x86

==================================== Bluebit .NET Matrix Library - 32 bit ==================================== This is a free version of .NET Matrix Library (NML™) which will allow matrix sizes up to 1000 x 1000. The Bluebit .NET Matrix Library (NML™) provides classes for object-oriented linear algebra in the .NET platform. It can be used to solve systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalues and eigenvectors problems, and singular value problems. Also provided are the associated matrix factorizations such as Eigen, LQ, LU, Cholesky, QR, SVD. .NET Matrix Library (NML™) also supports sparse matrices and advanced methods for solving large sparse systems of linear equations. The above functionality is present for both real and complex matrices. Two analogous sets of classes are provided for real and complex matrices, vectors and factorizations. While exposing an easy to use and powerful interface, the Bluebit .NET Matrix Library does not sacrifice any performance. Highly optimized BLAS and the standard LAPACK routines are used within the library and provide fast execution and accurate calculations. The Bluebit .NET Matrix Library has been developed as a mixed mode C++ project, combining together managed and unmanaged code and delivering the best of both worlds; the speed of native C++ code and the feature-rich and easy to use environment of the .NET Framework.

v7.0.1

↙ 3.7K / total

matrixmathlinearalgebraeigenvalues

CenterSpace.NMath.Standard.Windows.X86.X64

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.

v7.1.0.47

↙ 178.4K / total

mathstatisticsnumericsprobabilitylinear

CenterSpace.NMath.Standard.WindowsAndLinux.X64

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 x64 hardware. On Windows, requires Visual Studio 2017 C++ x64 runtime. On Linux, requires libgcc.

v7.1.0.47

↙ 142.8K / total

mathstatisticsnumericsprobabilitylinear

CenterSpace.NMath.Standard.OSX.X64

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 x64 hardware.

v7.2.0.35

↙ 105.9K / total

mathstatisticsnumericsprobabilitylinear

pkg

Universal.Common.Mathematics

Class library implementing advanced mathematical algorithms, transforms, and time series manipulations. Implementations favour simplicity and correctness.

v4.2.0

↙ 116.0K / total

MathematicsMatrixVectorIntervalAlgorithm

Dew.Math

Dew.Math is the Windows-optimized high-performance numerical computation library for .NET. It provides a vectorized matrix and vector math environment with native runtime acceleration, multithreaded execution, and extensive algorithm libraries for scientific, engineering, financial, AI/ML and signal processing workloads. Core Numerical Capabilities: - Dense linear algebra (BLAS, LAPACK): SVD, QR, LQ, LU, eigenvalue problems, least-squares, rank reveals - Sparse matrix support: direct solvers (Pardiso, UMFPACK), iterative solvers (CG, BiCG, GMRES), preconditioning strategies, structured sparse formats - Complex number computation with fully vectorized math operations - Polynomial arithmetic, interpolation, splines, rational approximations, Chebyshev basis transforms - Numerical differentiation, root solving, non-linear systems, ODE support for stiff and non-stiff cases - Probability distributions (over 30 families), random number generators, Monte Carlo methods - Special mathematical functions (Airy, Bessel, Gamma-related, elliptic integrals, Legendre, etc.) Optimization and Modeling: - Non-linear curve fitting with Levenberg-Marquardt and trust-region refinements - Direct and constrained optimization (Simplex/Nelder–Mead, BFGS, Conjugate Gradient, LP, dual-phase simplex, Gomory cutting plane) - Vectorized expression parser for dynamic formula construction and symbolic-style evaluation Performance Architecture: - Native accelerated BLAS/LAPACK kernels with automatic CPU dispatch (AVX, AVX2, AVX-512) - Scalable multithreading with a lock-free memory allocator for low-GC overhead - Optional OpenCL GPU offloading for supported device targets Platform Model: - Contains Windows native acceleration binaries - For Linux native acceleration use: Dew.Math.Linux - For a pure managed, portable edition use: Dew.Math.Core Use Dew.Math when you require **maximum numerical performance on Windows** for HPC, simulation, economic modeling, data analytics, or scientific visualization workflows.

v6.3.10

↙ 54.8K / total

dotnetcsharp

CenterSpace.NMath.Standard.Windows.X86

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 2017 C++ x86 runtime.

v7.1.0.47

↙ 41.8K / total

mathstatisticsnumericsprobabilitylinear

pkg

DotCompute.Algorithms

GPU-accelerated algorithms for DotCompute. Includes FFT, AutoDiff, sparse matrix operations, signal processing, and cryptographic primitives.

v0.6.2

↙ 2.3K / total

dotcomputealgorithmsfftautodiffsparse

pkg

BelNytheraSeiche.WaveletMatrix

A .NET library providing a high-performance Wavelet Matrix, Suffix Array, FM-Index, LCP Index and Burrows Wheeler Transform. Core Data Structures: - Wavelet Matrix: Generic implementation supporting various data types with efficient Rank, Select, Quant - Sparse Table: For fast Range Minimum Queries (RMQ) on static arrays. Text Analysis Components: - Suffix Array: The foundational class for most text analysis. It builds a Suffix Array and LCP Array from a given text, enabling fast substring searches. - LCP Index: Provides O(1) LCP queries between any two suffixes using a Sparse Table. - FM-Index: A full-text index based on the Burrows-Wheeler Transform, allowing efficient substring search operations (Count/Locate). Ideal for applications in bioinformatics, text processing, and data compression. For more details, please visit the project repository on GitHub.

v1.0.6

↙ 1.2K / total

stringologydata-structureswavelet-matrixsuffix-arraysa-is

pkg

BeamOs.CSparse.Interop

Dense LU, Cholesky and QR decomposition of real and complex linear systems.

v4.2.2

↙ 1.3K / total

mathsparsematrixlucholesky