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研究生: 吳孝仁
Wu, Xiao-Ren
論文名稱: Neural Network Approach for Nonlinear Complementarity Problem and Quadratic Programming with Second-Order Cone Constraints
Neural Network Approach for Nonlinear Complementarity Problem and Quadratic Programming with Second-Order Cone Constraints
指導教授: 陳界山
Chen, Jein-Shan
學位類別: 博士
Doctor
系所名稱: 數學系
Department of Mathematics
論文出版年: 2017
畢業學年度: 105
語文別: 英文
論文頁數: 90
中文關鍵詞: Nonlinear Complementarity ProblemSecond-Order ConeNeural Network
英文關鍵詞: Nonlinear Complementarity Problem, Second-Order Cone, Neural Network
DOI URL: https://doi.org/10.6345/NTNU202203035
論文種類: 學術論文
相關次數: 點閱:126下載:24
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  • 無中文摘要

    This dissertation focuses on two types of optimization problems, nonlinear complementarity problem (NCP for short) and quadratic programming with second-order cone constraints (SOCQP for short). Based on NCP-function and SOC-complementarity function, we propose suitable neural networks for each of them, respectively. For the NCP-function, we propose new one which is the generalization of natural residual function for NCP. It is a discrete generalization of natural residual function phinr, denoted as phinrp. Besides being a NCP-function, we also show its twice di erentiability and present the geometric view. In addition, we utilize neural network approach to solving nonlinear complementarity problems and quadratic programming problems with second-order cone constraints. By building neural networks based on di erent families of smooth NCP or SOCCP-functions. Our goal is to study the stability of the equilibrium with respect to di erent neural network models. Asymptotical stability are built in most neural network models. Under suitable conditions, we show the equilibrium being exponentially stable. Finally, the simulation results are reported to demonstrate the e ffectiveness of the proposed neural network.

    1 Introduction 4 2 Preliminaries 12 3 NCP-Functions: Generalized Natural Residual Function 15 3.1 Properties of Generalized Natural Residual Function phinrp 17 3.2 Geometric View of phinrp 23 3.3 Other types of NCP-Functions Based on NR and FB function 24 4 Neural Networks for Solving NCP by Smooth NCP-Functions 29 4.1 Background Materials for Stability Analysis 33 4.2 Dynamic System Based on  phinrp 36 4.3 Dynamic Systems Based on psisnrp and phidfbp 40 4.4 Numerical Experiments 44 5 Neural Networks for Solving SOCQP by Smooth SOC-Complementarity functions 63 5.1 Neural Network Model 64 5.2 Stability Analysis 66 5.3 Two Diff erent Families of Smooth SOC-Complementarity Functions 68 5.4 Numerical Experiments 71 6 Concluding Remarks 81 Bibliography 82

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