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.

This book presents the outcomes of the second edition of the International Conference on Intelligent Computing and Optimization (ICO) – ICO 2019, which took place on October 3–4, 2019, in Koh Samui, Thailand. Bringing together research scholars, experts, and investigators from around the globe, the conference provided a platform to share novel research findings, recent advances and innovative applications in the field. Discussing the need for smart disciplinary processes embedded into interdisciplinary collaborations in the context of meeting the growing global populations’ requirements, such as food and health care, the book highlights the role of intelligent computation and optimization as key technologies in decision-making processes and in providing cutting edge solutions to real-world problems.

Operation Research methods are often used in every field of modern life like industry, economy and medicine. The authors have compiled of the latest advancements in these methods in this volume comprising some of what is considered the best collection of these new approaches. These can be counted as a direct shortcut to what you may search for. This book provides useful applications of the new developments in OR written by leading scientists from some international universities. Another volume about exciting applications of Operations Research is planned in the near future. We hope you enjoy and benefit from this series!

This volume constitutes the proceedings of the First International EURO Mini Conference on Modelling and Simulation of Social-Behavioural Phenomena in Creative Societies, MSBC 2019, held in Vilnius, Lithuania, in September 2019. The 8 full papers and 2 short papers presented were carefully reviewed and selected from 26 submissions. The papers are organized in the following topical sections: computational intelligence in social sciences; modeling and analysis of social-behavioral processes.

Spline Regression Models shows how to use dummy variables to formulate and estimate spline regression models both in situations where the number and location of the spline knots are known in advance, and where estimation is required.

Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.

The investigation of nonlinear dynamis in physical and engineering systems from the point of view of systems and control theory is important to develop better engineering systems. Synchronization of oscillators and output regulation for rigid body systems are two problem classes which are inherently nonlinear and are of great importance in applications. This thesis contains novel results for both problem classes. In the case of sychronization of oscillators we consider two different system classes and give sufficient or necessary conditions for synchronization. In the case of the output regulation problems for rigid body systems we provide a new two-step control design procedure, a detailed analysis for the error dynamics and an application scenario for satellite control. A highlight of the thesis is a new separation principle which is the underlying principle of the two-step design procedure for the output regulation problem.