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 ScreensA screen searching for a synergy between mechanistically different precipitants, adapted from Majeed et al (2003). The original screen includes 64 conditions, rest of the block is ...

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

In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is ...

SparseP software package provides 25 SpMV kernels for real PIM systems supporting the four most widely used compressed matrix formats, and a wide range of data types. Our extensive evaluation provides ...

For dimensions p that can be much larger than the sample size n, we consider the estimation of sparse vectors θ*. Under matrix uncertainty, the Lasso and Dantzig selector turn out to be extremely ...