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 ...

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

This article proposes a constrained ℓ₁ minimization method for estimating a sparse inverse covariance matrix based on a sample of n iid p-variate random variables. The resulting estimator is shown to ...

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 ...

“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 ...

This is a preview. Log in through your library . Abstract There is increasing interest in the study of community detection for sparse networks. Here, we propose a new method for detecting communities ...