Open-source implementation of AlphaEvolve

OpenAlpha_Evolve is an open-source Python framework inspired by the groundbreaking research on autonomous coding agents like DeepMind's AlphaEvolve.

Iterative algorithms for solving linear systems, eigensystems, and singular value problems

LoongFlow: A Thinking & Learning Framework for Expert-Grade AI Agents.

BART: Toolbox for Computational Magnetic Resonance Imaging

Analyze Data with Pandas-based Networks. Documentation:

Julia code for the book Numerical Linear Algebra

Algebraic Multigrid in Julia

This is a Julia package of nonlinear solvers. These codes are used in my book, Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia.

A fast forward- and inverse kinematics solver for Arduino based on the FABRIK algorithm. The solver supports up to 4 DOF.

Numerical computation in native Haskell

This algorithm uses a rectangle made by the user to identify the foreground item. Then, the user can edit to add or remove objects to the foreground. Then, it removes the background and makes it transparent.

pySDC is a Python implementation of the spectral deferred correction (SDC) approach and its flavors, esp. the multilevel extension MLSDC and PFASST.

An iterative machine learning framework for predicting temperature profiles for an additive manufacturing process

Code for our work on pose-estimation using template 3D models.

This repository mirrors the principal Gitlab repository of the Chebyshev Accelerated Subspace iteration Eigensolver. If you want to contribute as developer to this project please contact e.di.napoli@fz-juelich.de.

General phase regularized MRI reconstruction using phase cycling

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

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

Flexible, non-allocating Julia implementations of the CG and BiCGStab methods.