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.

Assignments for CMA course from the BSU

KSPHPDDM and PCHPDDM

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

Julia package for periodic Schur decompositions of matrix products

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

modification of GMRES adapted from JuliaLinearAlgebra/IterativeSolvers.jl

The user friendly randomized numerical linear algebra package

Fortran/Python linear algebra utilities

Intro algorithms to iterative Krylov methods for solving large sparse systems

Research library for compile time optimization

Fitting STAR models using MCMC methods and Krylov subspace methods

A very high order FVM framework

Propagators for Quantum Dynamics and Optimal Control

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.

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.

Numerical linear algebra software package