Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...

Sparse matrix computations are prevalent in many scientific and technical applications. In many simulation applications, the solving of the sparse matrix-vector multiplication (SpMV) is critical for ...

This paper considers estimation of sparse covariance matrices and establishes the optimal rate of convergence under a range of matrix operator norm and Bregman divergence losses. A major focus is on ...

This paper presents a numerical comparison between algorithms for unconstrained optimization that take account of sparsity in the second derivative matrix of the objective function. Some of the ...

The classic sparse matrix screen based on Jancaric and Kim (1991) and modified by Cudney et al (1994). Samples salts, polymers, organics and pH (see conditions). Helsinki Random II A combined sparse ...

Recently, a research team from the FhG Institute for Applied Solid State Physics and the University of Cologne has made significant progress in the field of quantum computing by proposing a new ...

A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...

“Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield ...

Sparsity is a measured way of optimizing machine learning models by deliberately specifying which nodes are composed of zero values. Sparse models, as opposed to dense models, contain mostly zero ...