Working-with-sparse-matrices-in-MATLAB-and-R- Sometimes a researcher might find themselves needing to build an extremely large matrix that only contains a small number of non-zero values.

MATLAB toolbox for rank deficient sparse matrices: null spaces, reliable factorizations, etc. With Leslie Foster, San Jose State Univ. SSMULT C=A*B where A and B are both sparse. This was the basis ...

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

Summary <p>This chapter examines the seven variants of sparse matrices implemented in the Scipy library. These seven variants include: coordinate sparse matrix, compressed sparse column matrix, ...

RIKEN Center for Computational Science (R-CCS) will hold a hybrid school in January 2025 titled "RIKEN International HPC Spring School 2025 ~ Toward Society 5.0~ Parallel computation of ...

To reduce the interference of complex imaging environment on spectral unmixing, we introduce a double low-rank and sparse matrix factorization model (LRSMD) (DLRSMD) to better characterize the ...

We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization, and its adaptive version, for achieving sparsity when estimating the dimensions of a central subspace ...