There are 1 repository under krylov-methods topic.
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
PyGinkgo is a Python binding for the Ginkgo framework, providing access to Ginkgo's powerful linear algebra capabilities from Python.
Research library for compile time optimization
Intro algorithms to iterative Krylov methods for solving large sparse systems
Julia package for periodic Schur decompositions of matrix products
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.
Reference implementations of SBCGrQ and other Block Conjugate-Gradient iterative Krylov solvers in C++/Eigen
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