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.
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
Reference implementations of SBCGrQ and other Block Conjugate-Gradient iterative Krylov solvers in C++/Eigen
Fitting STAR models using MCMC methods and Krylov subspace methods
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
MATLAB package for F(A)*b with F a Laplace transform or complete Bernstein function
modification of GMRES adapted from JuliaLinearAlgebra/IterativeSolvers.jl
The user friendly randomized numerical linear algebra package
Add a description, image, and links to the krylov-methods topic page so that developers can more easily learn about it.
To associate your repository with the krylov-methods topic, visit your repo's landing page and select "manage topics."