There is increasing interest in the study of community detection for sparse networks. Here, we propose a new method for detecting communities in sparse networks that uses the symmetrized Laplacian ...

We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GPU. An efficient k-way merge lies at the heart of finding a fast parallel SpMSpV algorithm. We ...

Improve this page Add a description, image, and links to the sparse-matrix-addition topic page so that developers can more easily learn about it.

Decomposing sparse matrices into lower and upper triangular matrices (sparse LU factorization) is a key operation in many computational scientific applications. We developed SparseLU, a sparse linear ...

An artificial-intelligence approach known as AlphaTensor found exact matrix-multiplication algorithms that are more efficient than those previously known for many matrix sizes. The technique ...

In Section 2, we introduce our proposed methodology for co-sparse non-negative matrix factorization and develop an efficient iterative algorithm based on the primal-dual active set algorithm. Section ...

Matrix is widely used in telecommunication, cryptography, computer science and other field. Especially in wireless sensor network data processing, it is important and necessary to keep data ...

Researchers at MIT's Computer Science & Artificial Intelligence Lab (CSAIL) have open-sourced Multiply-ADDitioN-lESS (MADDNESS), an algorithm that speeds up machine learning using approximate ...

Add a description, image, and links to the sparse-matrix-addition topic page so that developers can more easily learn about it ...