HYPERGRAPH MODELS FOR SPARSE MATRIX PARTITIONING AND REORDERING  Umit V. C ataly urek Ph.D. in Computer Engineering and Information Science Supervisor: Assoc. Prof. Cevdet Aykanat November, 1999 Graphs have been widely used to represent sparse matrices for various scienti c applications including one-dimensional (1D) decomposition of sparse matrices for parallel sparse-matrix vector multiplic...

We introduce a new sparse sliced inverse regression estimator called Cholesky matrix penalization and its adaptive version for achieving sparsity in estimating the dimensions of central subspace. The estimators use decomposition covariance covariates include regularization term objective function to achieve computationally efficient manner. establish theoretical values tuning parameters that es...

Many scienti c applications require sparse matrix computations. For example, Finite Element modeling and N-body simulations. It is di cult to write these codes in a portable way which also achieves high performance because of the sparsity of the matrices and because current architectures have deep memory hierarchies and multiple levels of parallelism. Therefore the implementation of such comput...

Many scientiic applications require sparse matrix computations. For example, Finite Element model-ing and N-body simulations. It is diicult to write these codes in a portable way which also achieves high performance because of the sparsity of the matrices and because current architectures have deep memory hierarchies and multiple levels of parallelism. Therefore the implementation of such compu...