Sparse multi-dimensional arrays for the PyData ecosystem

PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations

sparse linear algebra library for rust

Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.

A Locality Sensitive Hashing (LSH) library with an emphasis on large, highly-dimensional datasets.

Sparse matrix formats for linear algebra supporting scientific and machine learning applications

SuiteSparse: a suite of sparse matrix packages by T. A. Davis et al. (This repository contains copies of the official versions.)

A massively parallel library for computing the functions of sparse matrices.

Efficient compression and retrieve of genotype data with integer sparse matrices

a Sparse Direct Solver modulo p

Attoparsec parsers for the NIST Matrix Market format

Multichannel Sleep Spindle Detector for sleep EEG

best CPU/GPU sparse solver for large sparse matrices

Approximate Minimum Degree Ordering in Julia

Sparse matrix handling library written for Java

An object oriented C++ header-only wrapper for Timothy A. Davis' Cholmod library (part of SuiteSparse)

High performance similarity and distance metrics for sparse representations

A little Julia module to allow factorization and backslash to work with hyperdual-valued arrays and sparse arrays.

A sparse matrix multiplication FPGA architecture which acts as a 'coprocessor'.

Sparse Binary Relation Representations for Genome Graph Annotation

Sparse data processing toolbox. It builds on top of pandas and scipy to provide DataFrame like API to work with sparse categorical data.

Presentation on how to build a recommender system @ PyData, Lisbon, Wed 13 Dec 2017

The SparseCollections library provides the SparseArray<T> and SparseMatrix<T> collection classes.

A sparse KLU solver for PyTorch.

Sparse Allreduce

R package for multivariate data handling with S4 classes and sparse matrices

E0 251 Data Structures and Algorithms course is offered by Department of Computer Science and automation, Indian Institute of Science, Bangalore. The assignment includes work on sparse data structures, large unsigned integer arithmetic, polynomial division, betweenness centrality using bfs and floyd warshall, persistent data structures, solving maze using persistent stack, user defined malloc and free.

Scientific Computing with Python

Implementations of Markov Clustrer Algorithm (MCL) and Regularized Markov Cluster Algorithm (R-MCL) in Apache Spark