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.

Sparse matrices are matrices that have mostly zero entries, and they often arise in numerical linear algebra when dealing with large-scale problems. For example, you might encounter sparse ...

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 ...

A novel Matlab routine, called FKMLO, is proposed to implement a variant of the standard Fuzzy K-Means algorithm. Such a variant allows for sparsity in the fuzzy membership degree matrix by adding a ...

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, ...

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 ...