Randomized Dimension Reduction Library
-
Updated
Apr 17, 2021 - Jupyter Notebook
Randomized Dimension Reduction Library
C++ Matrix -- High performance and accurate (e.g. edge cases) matrix math library with expression template arithmetic operators
nml is a "simple" matrix/numerical analysis library written in pure C. The scope of the library is to highlight various algorithm implementations related to matrices. Code readability was a major concern.
A concise library for solving sparse linear systems with direct methods.
Differentiable matrix factorizations using ImplicitDifferentiation.jl.
A powerful library extending VBA with over 100 functions for math, stats, finance, and data manipulation. It supports matrix operations, and user-defined functions, enhancing automation and analysis within Microsoft Office and LibreOffice environments for data management, financial calculations, an more.
A linear algebra library that provides a user-friendly interface to several BLAS and LAPACK routines.
Numerical analysis course in Python
This repository contains a Fortran implementation of a 2D flow using the projection method, with Finite Volume Method (FVM) approach. The code solves Navier Stokes equations in a 2D lid driven cavity, with computation of the rotational as well.
A linear algebra library for TypeScript and JavaScript
Minimalistic implementations of various algorithms for projects in machine learning and computer vision
A complete example of batched refactorization in cuSOLVER.
Built-in solvers for the GeoStats.jl framework
Fork of the qrupdate library for future maintenance.
Numerical Analysis Problems and Solutions
Rust sparse LU decomposition using Gilbert and Peierls method
Assignments for High Performace Computing exam at Unimore, Modena, IT.
Implementation for different numerical algorithms
Linear Equations solver project done using Matlab, uses different method to solve the equations as Gauss Elimination, Gauss Jordan, LU Decomposition, Gauss Seidel, and Jacobi Iterative Method
Add a description, image, and links to the lu-decomposition topic page so that developers can more easily learn about it.
To associate your repository with the lu-decomposition topic, visit your repo's landing page and select "manage topics."