Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • ログイン
    アイテム表示 
    •   ホーム
    • College of Computer, Mathematical & Natural Sciences
    • Computer Science
    • Technical Reports from UMIACS
    • アイテム表示
    •   ホーム
    • College of Computer, Mathematical & Natural Sciences
    • Computer Science
    • Technical Reports from UMIACS
    • アイテム表示
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Two Algorithms for the The Efficient Computation of Truncated Pivoted QR Approximations to a Sparse Matrix

    Thumbnail
    閲覧/開く
    CS-TR-3875.ps (112.4Kb)
    No. of downloads: 163

    Auto-generated copy of CS-TR-3875.ps (137.4Kb)
    No. of downloads: 520

    日付
    1998-10-15
    著者
    Stewart, G. W.
    Metadata
    アイテムの詳細レコードを表示する
    抄録
    In this note we propose two algorithms to compute truncated pivoted QR approximations to a sparse matrix. One is based on the Gram--Schmidt algorithm, and the other on Householder triangularization. Both algorithms leave the original matrix unchanged, and the only additional storage requirements are arrays to contain the factorization itself. Thus, the algorithms are particularly suited to determining low-rank approximations to a sparse matrix. (Also cross-referenced as UMIACS-TR-98-12)
    URI
    http://hdl.handle.net/1903/941
    Collections
    • Technical Reports from UMIACS
    • Technical Reports of the Computer Science Department

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    ブラウズ

    リポジトリ全体コミュニティ/コレクション公開日著者タイトル主題このコレクション公開日著者タイトル主題

    登録利用者

    ログイン登録
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility