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 ...

Which solver to choose depends on the nature of your matrix. SuiteSparse is built on top of LAPACK and BLAS, which are pretty low level and FORTRAN-y.

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 ...

This newly developed data processing utilizes computing and communications technologies that leverage “sparse matrix” data structures in order to significantly accelerate the performance of vector ...

Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the widely-used, memory-bound Sparse ...