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    Numerical Analysis in Nonlinear Least Squares Methods and Applications

    Eu C 2017.pdf (4.128Mb)
    Access Status
    Open access
    Authors
    Eu, Christina Nguk Ling
    Date
    2017
    Supervisor
    Prof. Bean San Goh
    Type
    Thesis
    Award
    MPhil
    
    Metadata
    Show full item record
    Faculty
    Engineering and Science (Sarawak)
    School
    Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/70491
    Collection
    • Curtin Theses
    Abstract

    The approximate greatest descent (AGD) method and a two-phase AGD method (AGDN) are proposed as new methods for a nonlinear least squares problem. Numerical experiments show that these methods outperform existing methods including the Levenberg-Marquardt method. However, the AGDN method outperforms the AGD method with a faster convergence. If the AGDN method fails due to singularity of the Hessian matrix, the AGD method should be used.

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