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 ...

In particular, we extend the DBCSR sparse matrix library, which is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. The library is ...

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 ...

Ph. L. Toint, Some Numerical Results Using a Sparse Matrix Updating Formula in Unconstrained Optimization, Mathematics of Computation, Vol. 32, No. 143 (Jul., 1978 ...

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 ...

A combined sparse matrix and incomplete factorial screens, samples pH 3.5 to 8.5, low ionic strength, high ionic strength and mixed polymer/salt conditions and halides for potential phasing (see ...