Backpropagate derivatives through the Cholesky decomposition

Sympiler is a Code Generator for Transforming Sparse Matrix Codes

Set up cholmod and scikit-sparse python package on Windows.

Sparse symmetric indefinite solver implemented with a runtime system

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.

Numerical methods for engineers used for finding roots, solving matrix, finding functions from given values, performing integrals whose analytical solution is exhaustive, and solutions by approximation for differential equations.

This package contains implementations of efficient representations and updating algorithms for Cholesky factorizations.

Rank-1 update and downdate of Cholesky factorization

Python and C# interoperability

JAMA : A Java Matrix Package. Fork of the original project.

Algoritmos de cálculo numérico usados para estudos e análise de complexidade

Repository for benchmarking linear solvers on GPU.

Compute the `L * D * L^T` factorization of a real symmetric positive definite tridiagonal matrix `A`.

Parallel Cholesky Factorization of a SPD Matrix with MPI

C# tool that computes Cholesky decomposition

Compute the `L * D * L^T` factorization of a real symmetric positive definite tridiagonal matrix `A`.

Cholesky decomposition for Hilbert matrix of any order in Python 3 (Two programs)

C++26 powered GPU-accelerated scientific apps

BSc Numerical Methods course report about Band Cholesky Method

Computational Linear Algebra course covering topics like iterative methods, matrix decompositions, and applications. It includes theoretical concepts, practical exercises, and code. Advanced methods like QR factorization, spectral theorem, and iterative solvers for linear systems.