This project aims utilize a sparse matrix as form of matrix or image value compression by basically implementing a special kind of data structure where it omits one continuously recurring value ultimately saving space only for "important" variables.

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

This code demonstrates the use of machine learning to model the multimodal nature of a single cell. Using machine learning to predict RNA from DNA, that is, using chromatin accessibility data to predict the RNA gene expression and to predict surface protein from RNA, that is, using RNA sequence data to predict surface protein levels in a cell

Algebraic Multigrid Solvers in Python

Kokkos C++ Performance Portability Programming Ecosystem: Math Kernels - Provides BLAS, Sparse BLAS and Graph Kernels

Fortran library to handle sparse matrices

Sparse multi-dimensional arrays for the PyData ecosystem

The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University.

DBCSR: Distributed Block Compressed Sparse Row matrix library

GMRES in rust using faer

Python package to accelerate the sparse matrix multiplication and top-n similarity selection

oj! Algorithms

SparseArrays.jl is a Julia stdlib

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Rust Scientific Libary. ODE and DAE (Runge-Kutta) solvers. Special functions (Bessel, Elliptic, Beta, Gamma, Erf). Linear algebra. Sparse solvers (MUMPS, UMFPACK). Probability distributions. Tensor calculus.

Pytorch Implementation of the sparse attention from the paper: "Generating Long Sequences with Sparse Transformers"

Compact Software Suite for Scientific Development - Linear Algebra

This code reads a matrix in CO-Ordinate (COO) format and writes the output in Compressed Sparse Row (CSR) format

PyTorch-Based Fast and Efficient Processing for Various Machine Learning Applications with Diverse Sparsity

Sparse vectors.