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
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.
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
A graduate numerical analysis course built as a runnable repository. 98 lessons in 14 parts, from binary numbers to Krylov eigensolvers, multigrid, randomized SVD and automatic differentiation. Every lesson is a notebook and a page to read, with from scratch code checked against SciPy, exercises at five levels and worked solutions.
Assignments for CMA course from the BSU
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