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ruvnet / sublinear-time-solver

Star 58

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.

pagerank-algorithm scientific-computing sparse-linear-solver numerical-linear-algebra rust-wasm iterative-solvers graph-laplacian sublinear-algorithms temporal-computing model-context-protocol mcp-tools diagonally-dominant conjugate-gradient-method neumann-series network-flow-optimization sparse-matrix-computation edge-computing-algorithms asymmetric-matrices distributed-solvers
  • Updated Oct 7, 2025
  • Rust

hiroyuki-kasai / SparseGDLibrary

Star 56

MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3

machine-learning big-data algorithms optimization machine-learning-algorithms solver lasso logistic-regression gradient-descent support-vector-machines admm proximal-algorithms proximal-operators sparse-regression optimization-algorithms matrix-completion elasticnet lasso-regression coordinate-descent sparse-linear-solver
  • Updated Nov 20, 2018
  • MATLAB
conjugate-gradient-sparse-cg-solver-course

SENATOROVAI / conjugate-gradient-sparse-cg-solver-course

Star 10

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

python optimization solver scientific-computing iterative-methods conjugate-gradient sparse-linear-solver numerical-linear-algebra matrix-algorithms preconditioned-conjugate-gradient large-scale-optimization optimization-solver senatorov preconditioned-cg python-cg-implementation iterative-solver
  • Updated Mar 1, 2026
  • Jupyter Notebook
LSQR-solver-course

SENATOROVAI / LSQR-solver-course

Star 10

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

python optimization solver least-squares scientific-computing iterative-methods sparse-linear-solver lsqr large-scale-optimizations numerical-linear-algebra large-scale-optimization matrix-computation senatorov lsqr-python-implementation lsqr-algorithm
  • Updated Mar 1, 2026
  • Python
AngstromCube

real-space / AngstromCube

Star 8

A parallel and GPU-accelerated Code for Real-Space All-Electron Linear-Scaling Density Functional Theory

spectrum openmp density-functional-theory gpu-acceleration finite-difference sparse-linear-systems electron-density sparse-matrix hamiltonian linear-solvers electronic-structure electronic-structure-calculations sparse-linear-solver pseudopotential cuda-programming projector-augmented-wave mpi-parallelization non-collinear-magnetism local-density-approximation tfqmr
  • Updated Feb 27, 2026
  • C++

DhruvSrikanth / Conjugate-Gradient-Simulation

Star 1

In this repository, you will find a serial, shared-memory parallel, distributed-memory parallel and hybrid implementations of the conjugate gradient optimization simulation. This is a sparse linear solver optimized using the second-order poisson equation.

c cpp optimization openmp mpi multithreading high-performance-computing conjugate-gradient optimization-algorithms poisson-equation simulation-modeling sparse-linear-solver shared-memory-parallel distributed-memory-parallel dense-solver
  • Updated Jun 6, 2022
  • C++

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