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.

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 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 ...

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 ...

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 ...