Download or read book Robust Optimization of Spline Models and Complex Regulatory Networks written by Ayşe Özmen and published by Springer. This book was released on 2016-05-11 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Intelligent Computing and Optimization written by Pandian Vasant and published by Springer Nature. This book was released on 2019-10-26 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Operations Research written by Vilda Purutçuoğlu and published by CRC Press. This book was released on 2022-11-24 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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!

Download or read book Modeling and Simulation of Social Behavioral Phenomena in Creative Societies written by Nitin Agarwal and published by Springer Nature. This book was released on 2019-09-11 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Spline Models for Observational Data written by Grace Wahba and published by SIAM. This book was released on 1990-01-01 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves well as an introduction into the more theoretical aspects of the use of spline models. It develops a theory and practice for the estimation of functions from noisy data on functionals. The simplest example is the estimation of a smooth curve, given noisy observations on a finite number of its values. The estimate is a polynomial smoothing spline. By placing this smoothing problem in the setting of reproducing kernel Hilbert spaces, a theory is developed which includes univariate smoothing splines, thin plate splines in d dimensions, splines on the sphere, additive splines, and interaction splines in a single framework. A straightforward generalization allows the theory to encompass the very important area of (Tikhonov) regularization methods for ill-posed inverse problems. Convergence properties, data based smoothing parameter selection, confidence intervals, and numerical methods are established which are appropriate to a wide variety of problems which fall within this framework. Methods for including side conditions and other prior information in solving ill-posed inverse problems are included. Data which involves samples of random variables with Gaussian, Poisson, binomial, and other distributions are treated in a unified optimization context. Experimental design questions, i.e., which functionals should be observed, are studied in a general context. Extensions to distributed parameter system identification problems are made by considering implicitly defined functionals.

Download or read book Optimization based Modeling in Investment and Data Science written by Qingyun Sun and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization has played a key role in numerous fields including data science, statistics, machine learning, decision science, control and quantitative investment. Optimization offers a way for users to focus on the modeling step. Convex optimization has been a very successful and powerful modeling framework. By formulating a problem as convex optimization, practitioners could focus on the modeling side without worrying about designing problem-specific optimization algorithms during prototyping time. However, there are hurdles in applying this convex modeling framework. First, lots of signal processing and machine learning problems are most naturally formulated as non-convex problems. Second, not all convex problems are tractable. Third, it may be hard to encode the knowledge of data into a simple regularizer or constraint and specify the mathematical form of the optimization problem. In this thesis, we talk about topics in optimization-based modeling, including 1) distributional robust Kelly strategy in investment and gambling; 2) convex sparse blind deconvolution; 3) missing data imputation via a new structure called matrix network; 4) neural proximal method for compressive sensing.In these works. I try to expand the boundary of convex optimization based modeling by conquering several hurdles. In the distributional robust Kelly problem, the original distributional robust optimization formulation isconvex but non-tractable; we transform the problem into a tractable form. In the sparse blind deconvolutionproblem, blind deconvolution has been perceived as a non-convex problem for a long time, we proposea scalable convex formulation, and find a phase transition for the convex algorithm. In the missing dataimputation problem, we study a slice-wise missing pattern on tensorial type data that is beyond the capabilityof typical tensor completion algorithms. We propose a new type of underlying low-dimensional structure thatallows us to impute the missing data. In the first three topics, we solve these problems via convex optimizationformulations. In the last topic, we step out of the safety zone of convexity. On the linear inverse problem, we go beyond the sparsity and1−norm regularizer for compressive sensing. To model complex structure innatural/medical images, we propose a learning-based idea to parameterize the proximal map of an unknownregularizer. This idea is inspired by the convex optimization modeling framework and the learning-basedmethod, although the result need not correspond to convex optimization.

Download or read book B spline Based Robust Formulation in Topology Optimization written by Yu Gu and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spline Regression Models written by Lawrence C. Marsh and published by SAGE. This book was released on 2001-09-14 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Mathematical Modelling and Parameter Inference of Genetic Regulatory Networks written by Qianqian Wu and published by . This book was released on 2015 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical modelling opens the door to a rich pathway to study the dynamic properties of biological systems. Among the many biological systems that would benefit from mathematical modelling, improving our understanding of gene regulatory networks has received much attention from the fields of computational biology and bioinformatics. To understand system dynamics of biological networks, mathematical models need to be constructed and studied. In spite of the efforts that have been given to explore regulatory mechanisms among gene net- works, accurate description of chemical events with multi-step chemical reactions still remains a challenge in biochemistry and biophysics. This dissertation is aimed at developing several novel methods for describing dynamics of multi-step chemical reaction systems. The main idea is introduced by a new concept for the location of molecules in the multi-step reactions, which is used as an additional indicator of system dynamics. Additionally, novel idea in the stochastic simulation algorithm is used to calculate time delay exactly, which shows that the value of time delay depends on the system states. All of these innovations alter the focus of originally complex multi-step structures towards defining novel simplified structures, which simplifies the modelling process significantly. Research results yield substantially more accurate results than published methods.Apart from the well-established knowledge for modelling techniques, there are still significant challenges in understanding the dynamics of systems biology. One of the major challenges in systems biology is how to infer unknown parameters in mathematical models based on experimental datasets, in particular, when data are sparse and networks are stochastic. To tackle this challenge, parameters estimation techniques using Approximate Bayesian Computation (ABC) for chemical reaction system and inference method for dynamic network have been investigated. This dissertation discusses developed ABC methods that have been tested on two stochastic systems. Results on artificial data show certain promising approximations for the unknown parameters in the systems. While unknown parameters are difficult and sometimes even impossible to measure with biological experiments, instead we can study the influence of parameter variation on system properties. Robustness and sensitivity are two major measurements to describe the dynamic properties of a system against the variation of model parameters. For stochastic models of discrete chemical reaction systems, although these two properties have been studied separately, no work has been done so far to investigate these two properties together. In this dissertation, An integrated framework has been proposed to study these two properties for the Nanog gene network simultaneously. It successfully identifies key coefficients that have more impacts on the network dynamics than the others. The proposed inference method to infer dynamic protein-gene interactions is applied to a case study of the human P53 protein, which is a well-known biological network for cancer study. Investigating the dynamics for such regulatory networks through high throughput experimental data has become more popular. To tackle the hindrances with large number of unknown parameters when building detailed mathematical models, a new integrated method is proposed by combining a top-down approach using probability graphical models and a bottom-up approach using differential equation models. Model simulation error, Akaike's information criterion, parameter identifiability and robustness properties are used as criteria to select the optimal network. Results based on random permutations of input gene network structures provide accurate prediction and robustness property. In addition, a comparison study suggests that the proposed approach has better simulation accuracy and robustness property than the earlier one. In particular, the computational cost is significantly reduced. Overall, the new integrated method is a promising approach for investigating the dynamics of genetic regulations.

Download or read book Synchronization of Oscillators and Global Output Regulation for Rigid Body Systems written by Gerd Simon Schmidt and published by Logos Verlag Berlin GmbH. This book was released on 2014 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Robust Optimization written by Aharon Ben-Tal and published by Princeton University Press. This book was released on 2009-08-10 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computer Control Abstracts written by and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scientific Computing with Ordinary Differential Equations written by Peter Deuflhard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Well-known authors; Includes topics and results that have previously not been covered in a book; Uses many interesting examples from science and engineering; Contains numerous homework exercises; Scientific computing is a hot and topical area

Download or read book Process Modelling and Simulation written by César de Prada and published by MDPI. This book was released on 2019-09-23 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since process models are nowadays ubiquitous in many applications, the challenges and alternatives related to their development, validation, and efficient use have become more apparent. In addition, the massive amounts of both offline and online data available today open the door for new applications and solutions. However, transforming data into useful models and information in the context of the process industry or of bio-systems requires specific approaches and considerations such as new modelling methodologies incorporating the complex, stochastic, hybrid and distributed nature of many processes in particular. The same can be said about the tools and software environments used to describe, code, and solve such models for their further exploitation. Going well beyond mere simulation tools, these advanced tools offer a software suite built around the models, facilitating tasks such as experiment design, parameter estimation, model initialization, validation, analysis, size reduction, discretization, optimization, distributed computation, co-simulation, etc. This Special Issue collects novel developments in these topics in order to address the challenges brought by the use of models in their different facets, and to reflect state of the art developments in methods, tools and industrial applications.

Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1978 with total page 990 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Seamless R and C Integration with Rcpp written by Dirk Eddelbuettel and published by Springer Science & Business Media. This book was released on 2013-06-04 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rcpp is the glue that binds the power and versatility of R with the speed and efficiency of C++. With Rcpp, the transfer of data between R and C++ is nearly seamless, and high-performance statistical computing is finally accessible to most R users. Rcpp should be part of every statistician's toolbox. -- Michael Braun, MIT Sloan School of Management "Seamless R and C++ integration with Rcpp" is simply a wonderful book. For anyone who uses C/C++ and R, it is an indispensable resource. The writing is outstanding. A huge bonus is the section on applications. This section covers the matrix packages Armadillo and Eigen and the GNU Scientific Library as well as RInside which enables you to use R inside C++. These applications are what most of us need to know to really do scientific programming with R and C++. I love this book. -- Robert McCulloch, University of Chicago Booth School of Business Rcpp is now considered an essential package for anybody doing serious computational research using R. Dirk's book is an excellent companion and takes the reader from a gentle introduction to more advanced applications via numerous examples and efficiency enhancing gems. The book is packed with all you might have ever wanted to know about Rcpp, its cousins (RcppArmadillo, RcppEigen .etc.), modules, package development and sugar. Overall, this book is a must-have on your shelf. -- Sanjog Misra, UCLA Anderson School of Management The Rcpp package represents a major leap forward for scientific computations with R. With very few lines of C++ code, one has R's data structures readily at hand for further computations in C++. Hence, high-level numerical programming can be made in C++ almost as easily as in R, but often with a substantial speed gain. Dirk is a crucial person in these developments, and his book takes the reader from the first fragile steps on to using the full Rcpp machinery. A very recommended book! -- Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark "Seamless R and C ++ Integration with Rcpp" provides the first comprehensive introduction to Rcpp. Rcpp has become the most widely-used language extension for R, and is deployed by over one-hundred different CRAN and BioConductor packages. Rcpp permits users to pass scalars, vectors, matrices, list or entire R objects back and forth between R and C++ with ease. This brings the depth of the R analysis framework together with the power, speed, and efficiency of C++. Dirk Eddelbuettel has been a contributor to CRAN for over a decade and maintains around twenty packages. He is the Debian/Ubuntu maintainer for R and other quantitative software, edits the CRAN Task Views for Finance and High-Performance Computing, is a co-founder of the annual R/Finance conference, and an editor of the Journal of Statistical Software. He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.