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.

#Overview This program implements the representation of a sparse matrix using arrays. Sparse matrices contain a large number of zero elements, and storing these zeros is inefficient in terms of memory ...

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, ...

The importance and presence of graph theory in many different scientific areas has led to the development and use of various software tools, intended to explain graph concepts and graph algorithms.

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

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 ...

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 ...