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Robust Optimization of Spline Models and Complex Regulatory Networks

Theory, Methods and Applications

  • Book
  • © 2016

Overview

Authors:
  1. Ayse Özmen

    1. Ankara, Turkey

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  • new methods of robust optimization to handle uncertainty and non-linearity in
  • complex regulatory networks
  • Provides guidance in the trade-off between accuracy and robustness
  • Exemplifies the new methods in three detailed applications involving financial, energy and environmental systems

Part of the book series: Contributions to Management Science (MANAGEMENT SC.)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xii
    Download chapter PDF

  2. Introduction

    • Ayşe Özmen
    Pages 1-7

  3. Mathematical Methods Used

    • Ayşe Özmen
    Pages 9-33

  4. New Robust Analytic Tools

    • Ayşe Özmen
    Pages 35-57

  5. Spline Regression Models for Complex Multi-Model Regulatory Networks

    • Ayşe Özmen
    Pages 59-71

  6. Robust Optimization in Spline Regression Models for Regulatory Networks Under Polyhedral Uncertainty

    • Ayşe Özmen
    Pages 73-87

  7. Real-World Application with Our Robust Tools

    • Ayşe Özmen
    Pages 89-113

  8. Conclusion and Outlook

    • Ayşe Özmen
    Pages 115-117

  9. Back Matter

    Pages 119-139
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Keywords

About this book

This book introduces methods of robust optimization in multivariate adaptive regression splines (MARS) and Conic MARS in order to handle uncertainty and non-linearity. The proposed techniques are implemented and explained in two-model regulatory systems that can be found in the financial sector and in the contexts of banking, environmental protection, system biology and medicine. The book provides necessary background information on multi-model regulatory networks, optimization and regression. It presents the theory of and approaches to robust (conic) multivariate adaptive regression splines - R(C)MARS – and robust (conic) generalized partial linear models – R(C)GPLM – under polyhedral uncertainty. Further, it introduces spline regression models for multi-model regulatory networks and interprets (C)MARS results based on different datasets for the implementation. It explains robust optimization in these models in terms of both the theory and methodology. In this context it studies R(C)MARS results with different uncertainty scenarios for a numerical example. Lastly, the book demonstrates the implementation of the method in a number of applications from the financial, energy, and environmental sectors, and provides an outlook on future research.

Authors and Affiliations

  • Ankara, Turkey

    Ayse Özmen

About the author

Ayşe Özmen has affiliation at Turkish Energy Foundation(TENVA)and Institute of Applied Mathematics of Middle East Technical University (METU), Ankara, Turkey. Her research is on OR, optimization, energy modelling, renewable energy systems, network modelling, regulatory networks, data mining. She received her Doctorate in Scientific Computing at Institute for Applied Mathematics at METU. 

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