# GSoC 2017: Implementing iterative solvers for numerical linear algebra

DevFeed: [GSoC 2017: Implementing iterative solvers for numerical linear algebra](<https://devfeed.tech/articles/gsoc-2017-implementing-iterative-solvers-for-numerical-linear-algebra-46262.md>)

Original publisher: [Read original article](<https://julialang.org/blog/2017/08/native-julia-implementations-of-iterative-solvers-for-numerical-linear-algebra/index.html>)

Author: Harmen Stoppels; Andreas Noack

Published: 2017-08-23T00:00:00Z

Content type: article

Language: en

Sources: [JuliaLang - The Julia programming language](<https://devfeed.tech/sources/julialang-the-julia-programming-language.md>)

Topics: [The Julia Language](<https://devfeed.tech/topics/julia.md>), [implementation](<https://devfeed.tech/topics/implementation.md>)

Tags: [chebyshev](<https://devfeed.tech/tags/chebyshev.md>), [eigenvalues](<https://devfeed.tech/tags/eigenvalues.md>), [gsoc](<https://devfeed.tech/tags/gsoc.md>), [implementing](<https://devfeed.tech/tags/implementing.md>), [linear-algebra](<https://devfeed.tech/tags/linear-algebra.md>), [matrices](<https://devfeed.tech/tags/matrices.md>), [matrix](<https://devfeed.tech/tags/matrix.md>), [memory](<https://devfeed.tech/tags/memory.md>), [optimizations](<https://devfeed.tech/tags/optimizations.md>)

## AI overview

A GSoC project report describing native Julia implementations of the Jacobi-Davidson method and incomplete LU factorization for sparse matrices. It also covers improvements to iterative solvers, performance, memory usage, Julia compatibility, and documentation.

## Source excerpt

GSoC 2017: Implementing iterative solvers for numerical linear algebra | The central part of my GSoC project is about implementing the Jacobi-Davidson method natively in Julia, available in JacobiDavidson.jl (https://github.com/haampie/JacobiDavidson.jl). This method computes a few approximate solutions of the eigenvalue problem Ax = lambda Bx for large and sparse mat...