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  1. High dimensional factor models with an application to mutual fund characteristics
    Author: Lettau, Martin
    Published: 07 March 2022
    Publisher:  Centre for Economic Policy Research, London

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    Verlag (lizenzpflichtig)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    LZ 161
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    Location:
    Universitätsbibliothek Mannheim
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    No inter-library loan
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Discussion paper series / Centre for Economic Policy Research ; DP17091$p
    Subjects: Tucker decomposition; CP decomposition; tensors; PCA; SVD; factor models; Mutualfunds; Characteristics
    Scope: 1 Online-Ressource (circa 54 Seiten), Illustrationen
  2. Surrogate models for optimization of dynamical systems
    Author: Khowaja, Kainat; Ščerbatij, Michajlo; Härdle, Wolfgang
    Published: [2021]
    Publisher:  International Research Training Group 1792, Berlin

    Driven by increased complexity of dynamical systems, the solution of system of differential equations through numerical simulation in optimization problems has become computationally expensive. This paper provides a smart data driven mechanism to... more

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 744
    Inter-library loan:
    No inter-library loan

     

    Driven by increased complexity of dynamical systems, the solution of system of differential equations through numerical simulation in optimization problems has become computationally expensive. This paper provides a smart data driven mechanism to construct low dimensional surrogate models. These surrogate models reduce the computational time for solution of the complex optimization problems by using training instances derived from the evaluations of the true objective functions. The surrogate models are constructed using combination of proper orthogonal decomposition and radial basis functions and provides system responses by simple matrix multiplication. Using relative maximum absolute error as the measure of accuracy of approximation, it is shown surrogate models with latin hypercube sampling and spline radial basis functions dominate variable order methods in computational time of optimization, while preserving the accuracy. These surrogate models also show robustness in presence of model non-linearities. Therefore, these computational efficient predictive surrogate models are applicable in various fields, specifically to solve inverse problems and optimal control problems, some examples of which are demonstrated in this paper.

     
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/230835
    Series: IRTG 1792 discussion paper ; 2021, 001
    Subjects: Proper Orthogonal Decomposition; SVD; Radial Basis Functions; Optimization; Surrogate Models; Smart Data Analytics; Parameter Estimation
    Scope: 1 Online-Ressource (circa 31 Seiten), Illustrationen