Partitioning sparse rectangular matrices for parallel processing | Zendy

Tamara G. Kolda | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Partitioning sparse rectangular matrices for parallel processing

Author(s) -

Tamara G. Kolda

Publication year - 1998

Publication title -

osti oai (u.s. department of energy office of scientific and technical information)

Language(s) - English

Resource type - Reports

DOI - 10.2172/658436

Subject(s) - computer science , matrix (chemical analysis) , square (algebra) , algorithm , sparse matrix , square matrix , parallel computing , mathematics , symmetric matrix , eigenvalues and eigenvectors , geometry , materials science , composite material , gaussian , physics , quantum mechanics

The authors are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. They will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. They will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods -- the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research