Search Results - (Author, Cooperation:D. Pu)

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  1. 1
    Staff View
    Publication Date:
    2016-04-12
    Publisher:
    Nature Publishing Group (NPG)
    Print ISSN:
    0028-0836
    Electronic ISSN:
    1476-4687
    Topics:
    Biology
    Chemistry and Pharmacology
    Medicine
    Natural Sciences in General
    Physics
    Keywords:
    Aerosols/chemistry ; Atlantic Ocean ; Atmosphere/*chemistry ; Nitrates/analysis/chemistry ; Nitric Acid/chemistry ; Nitrogen/*analysis/*chemistry ; Nitrogen Oxides/*analysis/*chemistry ; Nitrous Acid/analysis/chemistry ; North Carolina ; Oxidants/chemistry ; Photolysis ; Seawater/*chemistry ; South Carolina
    Published by:
    http://www.nature.com/

    Latest Papers from Table of Contents or Articles in Press
  2. 2
    C. Ye ; X. Zhou ; D. Pu ; J. Stutz ; J. Festa ; M. Spolaor ; C. Cantrell ; R. L. Mauldin ; A. Weinheimer ; J. Haggerty
    American Association for the Advancement of Science (AAAS)
    Published 2015
    Staff View
    Publication Date:
    2015-06-20
    Publisher:
    American Association for the Advancement of Science (AAAS)
    Print ISSN:
    0036-8075
    Electronic ISSN:
    1095-9203
    Topics:
    Biology
    Chemistry and Pharmacology
    Computer Science
    Medicine
    Natural Sciences in General
    Physics
    Published by:
    http://www.aaas.org/

    Latest Papers from Table of Contents or Articles in Press
  3. 3
    Pu, D. ; Zhang, J.
    Springer
    Published 2000
    Staff View
    ISSN:
    1573-2878
    Keywords:
    nonsmooth optimization ; inexact Newton methods ; generalized Newton methods ; global convergence ; superlinear rate
    Source:
    Springer Online Journal Archives 1860-2000
    Topics:
    Mathematics
    Notes:
    Abstract Motivated by the method of Martinez and Qi (Ref. 1), we propose in this paper a globally convergent inexact generalized Newton method to solve unconstrained optimization problems in which the objective functions have Lipschitz continuous gradient functions, but are not twice differentiable. This method is implementable, globally convergent, and produces monotonically decreasing function values. We prove that the method has locally superlinear convergence or even quadratic convergence rate under some mild conditions, which do not assume the convexity of the functions.
    Type of Medium:
    Electronic Resource
    URL:
    http://dx.doi.org/10.1023/A:1004605428858

    Articles: DFG German National Licenses