Robust Optimization of Spline Models and Complex Regulatory Networks

Theory, Methods and Applications
Softcover Nachdruck of the original 1. Auflage 2016
ISBN: 978-3-319-80890-1
Verlag: Springer International Publishing

Buch, Englisch, 139 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 2409 g

Reihe: Contributions to Management Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Özmen, Ay¿e

Özmen, Ayse

Fachgebiete

• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Mathematische Modellierung
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik Mathematik Operations Research

Inhaltsverzeichnis

Introduction.- Mathematical Methods Used.- New Robust Analytic Tools.- Spline Regression Models for Complex Multi-Model Regulatory Networks.- Robust Optimization in Spline Regression Models for Regulatory Networks Under Polyhedral Uncertainty.- Real-World Application with Our Robust Tools.- Conclusion and Outlook.

Über Autor(innen)

Ayse 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.