LAPACK development repository
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Updated
Aug 11, 2024 - Fortran
LAPACK development repository
开源Go语言数值算法库(An open numerical library purely based on Go programming language)
Solving linear, nonlinear equations, ordinary differential equations, ... using numerical methods in fortran
Matrix Functions for Teaching and Learning Linear Algebra and Multivariate Statistics
Implementation of ConjugateGradients method using C and Nvidia CUDA
Modularized Fortran LAPACK implementation
JS ND-Arrays inspired by NumPy.
Modern Fortran sparse linear systems solver
Various Small Projects on Various Subjects
Methods of numerical modelling with undergraduate level computational physics solutions with non-linear equations, gaussian probabilities, and Euler RK4 integration
Numerical Methods (NM) for BE Electrical II Year / II Part, Email: [email protected]
This is a Git repository containing code for a numerical analysis lab using Fortran and MATLAB
Set of simple mathematical classes in C# (Vectors, Matrixes, Polynoms, Systems of linear equations, Integrals methods, Complex numbers, Rational numbers, Graphs, Methods for solving differential equations) + some features such as memoize (function values memorising)
Fast routines for solving large systems of linear equations in R. Makes Eigen Cholesky-, LU-, QR-, and iterative (Conjugate Gradient, BiCGSTAB) solvers for both dense and sparse problems available.
monolithic linear solver based on domain decomposition
Implentation of Back Substitution, Conjugate Gradient and Gauss Seidel using OpenMP parallelization
Laboratory work on the mathematics course at ITMO University, completed in the second semester in 2022.
oomph-lib is an object-oriented, open-source finite-element library for the simulation of multi-physics problems. It is developed and maintained by Matthias Heil and Andrew Hazel of the School of Mathematics at The University of Manchester, along with many other contributors.
Fortran Package Manager version of LSMR: Sparse Equations and Least Squares
Collection of algorithms discussed in "Numerical Methods" course
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