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Numerical Algorithms
Erschienen in: Numerical Algorithms 1/2021

12.02.2020 | Original Paper

Flattened aggregate function method for nonlinear programming with many complicated constraints

verfasst von: Xiaowei Jiang, Yueting Yang, Yunlong Lu, Mingyuan Cao

Erschienen in: Numerical Algorithms | Ausgabe 1/2021

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Abstract

In this paper, efforts are made to solve nonlinear programming with many complicated constraints more efficiently. The constrained optimization problem is firstly converted to a minimax problem, where the max-value function is approximately smoothed by the so-called flattened aggregate function or its modified version. For carefully updated aggregate parameters, the smooth unconstrained optimization problem is solved by an inexact Newton method. Because the flattened aggregate function can usually reduce greatly the amount of computation for gradients and Hessians, the method is more efficient. Convergence of the proposed method is proven and some numerical results are given to show its efficiency.
Vorheriger Artikel A numerically efficient variational algorithm to solve a fractional nonlinear elastic string equation
Nächster Artikel Superconvergence analysis of two-grid methods for bacteria equations

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Metadaten
Titel
Flattened aggregate function method for nonlinear programming with many complicated constraints
verfasst von
Xiaowei Jiang
Yueting Yang
Yunlong Lu
Mingyuan Cao
Publikationsdatum
12.02.2020
Verlag
Springer US
Erschienen in
Numerical Algorithms / Ausgabe 1/2021
Print ISSN: 1017-1398
Elektronische ISSN: 1572-9265
DOI
https://doi.org/10.1007/s11075-020-00881-1

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