Numerical linear algebra software package

LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.

Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.

Stability, bifurcation, and sensitivity analysis for Nek5000

PyGinkgo is a Python binding for the Ginkgo framework, providing access to Ginkgo's powerful linear algebra capabilities from Python.

Propagators for Quantum Dynamics and Optimal Control

A very high order FVM framework

Research library for compile time optimization

Julia package for periodic Schur decompositions of matrix products

Intro algorithms to iterative Krylov methods for solving large sparse systems

Fortran/Python linear algebra utilities

In this project I implement a CUDA Lanczos method to approximate the matrix exponential. The matrix exponential is an important centrality measure for large, sparse graphs.

Fitting STAR models using MCMC methods and Krylov subspace methods

KSPHPDDM and PCHPDDM

Reference implementations of SBCGrQ and other Block Conjugate-Gradient iterative Krylov solvers in C++/Eigen

The user friendly randomized numerical linear algebra package

High-performance quantum simulator for matrix-free Hamiltonian evolution

Assignments for CMA course from the BSU

MATLAB package for F(A)*b with F a Laplace transform or complete Bernstein function