Julia code for the book Numerical Linear Algebra

Own solutions for exercises and MATLAB example codes for "Numerical Linear Algebra" by Lloyd N. Trefethen and David Bau III, 1997

A header-only C++ library for sketching in randomized linear algebra

Learning some numerical linear algebra.

A concise library for solving sparse linear systems with direct methods.

Rust + WASM sublinear-time solver for asymmetric diagonally dominant systems. Exposes Neumann series, push, and hybrid random-walk algorithms with npm/npx CLI and Flow-Nexus HTTP streaming for swarm cost propagation and verification.

IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)

A scalable eigensolver for dense, symmetric (hermitian) matrices (fork of https://gitlab.mpcdf.mpg.de/elpa/elpa.git)

A Julia package for hierarchically semiseparable (HSS) matrices.

Randomized singular value decomposition (SVD) written in C++14 / Eigen

This repository is all the code to go along with my video series on computational Linear algebra.

📕 Proof-Based Math Readings is a free, independent online reading group where we study the mathematics required for economics master’s and PhD programs through an intuitive approach. Active since May 2023.

QR decomposition, or QR factorization, is a fundamental linear algebra method that decomposes a matrix into a product of an orthogonal matrix and an upper triangular matrix. It is widely used for solving linear least squares problems, computing eigenvalues, Gram-Schmidt, Householder reflections, or Givens rotations.Solver

LSQR is an iterative method for solving large, sparse, linear systems of equations and linear least-squares problems, including under- or over-determined and rank-deficient systems. It uses the Lanczos bidiagonalization process to provide a robust alternative to conjugate gradients, offering better numerical stability. Solver

Using the numerical linear algebra software package Ginkgo in Julia!

Singular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data reduction, noise removal, and matrix approximation.Solver

The Conjugate Gradient (CG) method is an efficient iterative algorithm for solving large, sparse systems of linear equations where the matrix is symmetric and positive-definite. It finds the minimum of a quadratic function by generating conjugate search directions, ensuring convergence in at most steps for an matrix.Solver

An improved incremental singular value decomposition(SVD) algorithm

A Nanofactory Roadmap 2: Improved Proposal for a Comprehensive Diamondoid Nanofactory Development Program

Educational linear algebra algorithms

Fast linear algebra for Java

Numerical Linear Algebra

Numerical experiments on Jacobi SVD algorithm

Python implementation of RLS-Nystrom

Computatinal physics University of Oslo

Hierarchical solvers is an approximate sparse direct solver, written entirely in Julia.

No description provided.

MEPACK: Matrix Equation PACKage

Main repository of the PSCToolkit package, it contains pointer to all the various part of the library.

Matrix function for PSBLAS