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Linear Programming : Foundations and Extensions
Linear Programming: Foundations and Extensions is an introduction to the field of optimization. The book emphasizes constrained optimization, beginning with a substantial treatment of linear programming, and proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. The book is carefully written. Specific examples and concrete algorithms precede more abstract topics. Topics are clearly developed with a large number of numerical examples worked out in detail.
Linear Optimization Problems with Inexact Data
Linear programming attracted the interest of mathematicians during and after World War II when the first computers were constructed and methods for solving large linear programming problems were sought in connection with specific practical problems—for example, providing logistical support for the U.S. Armed Forces or modeling national economies. Early attempts to apply linear programming methods to solve practical problems failed to satisfy expectations. There were various reasons for the failure. One of them, which is the central topic of this book, was the inexactness of the data used to create the models. This phenomenon, inherent in most pratical problems, has been dealt with in several ways. At first, linear programming models used "average” values of inherently vague coefficients, but the optimal solutions of these models were not always optimal for the original problem itself. Later researchers developed the stochastic linear programming approach, but this too has its limitations. Recently, interest has been given to linear programming problems with data given as intervals, convex sets and/or fuzzy sets. The individual results of these studies have been promising, but the literature has not presented a unified theory. Linear Optimization Problems with Inexact Data attempts to present a comprehensive treatment of linear optimization with inexact data, summarizing existing results and presenting new ones within a unifying framework.
Linear Models for Optimal Test Design
Begins with a reflection on the history of test design--the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples. The presentation includes models for the assembly of tests to an absolute or relative target for their information functions, classical test assembly, test equating problems, item matching, test splitting, simultaneous assembly of multiple tests, tests with item sets, multidimensional tests, and adaptive test assembly. Two separate chapters are devoted to the questions of how to design item banks for optimal support of programs with fixed and adaptive tests. Linear Models for Optimal Test Design, which does not require any specific mathematical background, has been written to be a helpful resource on the desk of any test specialist.
Linear and Nonlinear Programming
"Linear and Nonlinear Programming" is considered a classic textbook in Optimization. While it is a classic, it also reflects modern theoretical insights. These insights provide structure to what might otherwise be simply a collection of techniques and results, and this is valuable both as a means for learning existing material and for developing new results. One major insight of this type is the connection between the purely analytical character of an optimization problem, expressed perhaps by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. This was a major theme of the first and second editions. Now the third edition has been completely updated with recent Optimization Methods. Yinyu Ye has written chapters and chapter material on a number of these areas including Interior Point Methods.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.
Linear and Generalized Linear Mixed Models and Their Applications
This book covers two major classes of mixed effects models—linear mixed models and generalized linear mixed models—and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
Level Crossing Methods in Stochastic Models
Since its inception in 1974, the level crossing approach for analyzing a large class of stochastic models has become increasingly popular among researchers. This volume traces the evolution of level crossing theory for obtaining probability distributions of state variables and demonstrates solution methods in a variety of stochastic models including: queues, inventories, dams, renewal models, counter models, pharmacokinetics, and the natural sciences. Results for both steady-state and transient distributions are given, and numerous examples help the reader apply the method to solve problems faster, more easily, and more intuitively.
Large-Scale Nonlinear Optimization
Large-Scale Nonlinear Optimization reviews and discusses recent advances in the development of methods and algorithms for nonlinear optimization and its applications, focusing on the large-dimensional case, the current forefront of much research.The chapters of the book including theoretical analysis, algorithmic development, implementation issues and applications.
Killer Cell Dynamics : Mathematical and Computational Approaches to Immunology
Reviews how mathematics can be used in combination with biological data in order to improve understanding of how the immune system works. This is illustrated largely in the context of viral infections. Mathematical models allow scientists to capture complex biological interactions in a clear mathematical language and to follow them to their precise logical conclusions. This can give rise to counter-intuitive insights which would not be attained by experiments alone, and can be used for the design of further experiments in order to address the mathematical results.
Just-in-Time Scheduling : Models and Algorithms for Computer and Manufacturing Systems
As the field of Supply Chain Management has matured, maintaining the precise flow of goods to maintain schedules (hence, minimizing inventories) on a just-in-time basis still remains as a major challenge. This problem or challenge has resulted in a fair amount of quantitative research in the area, producing an array of models and algorithms to help ensure the precise flow of components and final products into inventories to meet just-in-time requirements.The scheduling models and algorithms presented and illustrated in the book will be done so in the context of extensive use of computer systems in a "real time context.
IUTAM symposium on topological design optimization of structures, machines and materials ; Status and perspectives
Contains the refereed and edited versions of papers presented at the IUTAM Symposium on Topological Design Optimization of Structures. The IUTAM Symposium provided a forum for the exchange of ideas for - ture developments in the area of topological design optimization. This enc- passed the application to ?uid-solid interaction problems, acoustics problems, and to problems in biomechanics, as well as to other multiphysics problems.
IUTAM Symposium on Multiscale Modelling of Damage and Fracture Processes in Composite Materials ; Proceedings of the IUTAM Symposium held in Kazimierz Dolny, Poland, 23-27 May 2005
This volume constitutes the Proceedings of the IUTAM Symposium. The main aim of the Symposium was to discuss the basic principles of damage growth and fracture processes in different types of composites: ceramic, polymer and metal matrix composites, cement and bituminous composites and wood. Nowadays, it is widely recognized that important macroscopic properties like the macroscopic stiffness and strength, are governed by processes that occur at one to several scales below the level of observation starting from nanoscale.
Iterative Learning Control : Robustness and Monotonic Convergence for Interval Systems
This monograph studies the design of robust, monotonically-convergent iterative learning controllers for discrete-time systems. Two key problems with the fundamentals of iterative learning control (ILC) design as treated by existing work are: first, many ILC design strategies assume nominal knowledge of the system to be controlled and; second, it is well-known that many ILC algorithms do not produce monotonic convergence, though in applications monotonic convergence is often essential. Iterative Learning Control takes account of the recently-developed comprehensive approach to robust ILC analysis and design established to handle the situation where the plant model is uncertain. Considering ILC in the iteration domain, it presents a unified analysis and design framework that enables designers to consider both robustness and monotonic convergence for typical uncertainty models, including parametric interval uncertainties, iteration-domain frequency uncertainty, and iteration-domain stochastic uncertainty.
COMPSTAT 2008 ; Proceedings in Computational Statistics
Presents methodological developments in Applied/Computational Statistics. This work covers a range of topics including Advances on Statistical Computing Environments, Methods for Classification and Clustering, Computation for Graphical Models and Bayes Nets, Computational Econometrics, and, Computational Statistics and Data Mining.
Complex-valued neural networks
This book is the first monograph ever on complex-valued neural networks, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. It is useful for those beginning their studies, for instance, adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, brainlike information processing, robotics inspired by human neural systems, and interdisciplinary studies to realize comfortable society. It is also helpful to those who carry out research and development regarding new products and services at companies.
Complexity Explained
This book explains why complex systems research is important in understanding the structure, function and dynamics of complex natural and social phenomena. It illuminates how complex collective behavior emerges from the parts of a system, due to the interaction between the system and its environment.
Compatible Spatial Discretizations
Compatible spatial discretizations are those that inherit or mimic fundamental properties of the PDE such as topology, conservation, symmetries, and positivity structures and maximum principles. It offer a snapshot of the current trends and developments in compatible spatial discretizations. The reader will find valuable insights on spatial compatibility from several different perspectives and important examples of applications compatible discretizations in computational electromagnetics, geosciences, linear elasticity, eigenvalue approximations and MHD. The contributions collected in this volume will help to elucidate relations between different methods and concepts and to generally advance our understanding of compatible spatial discretizations for PDEs.
Characteristic based planning with mySAP SCM™ : Scenarios, processes, and functions
Characteristics are used in SAP as attributes, e.g. to specify the configuration of products or the properties of batches. In many industries – engineering, automotive, mill, pharmaceutical and foods to name the most typical – supply chain planning has to consider these characteristics. APO offers many different functionalities for planning with characteristics, where each of the functionalities has some prerequisites and incompatibilities. This book offers help and advice for the basic design of the implementation by explaining the processes and scenarios (process chains) for planning with characteristics, the functionalities for planning with characteristics in APO including their prerequisites and incompatibilities and the entities, dependencies and system configuration determinants for planning with characteristics in R/3 and APO. This book is based on the releases R/3 4.7 and mySAP SCM 4.1.
Chaos : A Program Collection for the PC
This new edition strives yet again to provide readers with a working knowledge of chaos theory and dynamical systems through parallel introductory explanations in the book and interaction with carefully-selected programs supplied on the accompanying diskette. The programs enable readers, especially advanced-undergraduate students in physics, engineering, and math, to tackle relevant physical systems quickly on their PCs, without distraction from algorithmic details. For the third edition of Chaos: A Program Collection for the PC, each of the previous twelve programs is polished and rewritten in C++ (both Windows and Linux versions are included). A new program treats kicked systems, an important class of two-dimensional problems, which is introduced in Chapter 13. Each chapter follows the structure: theoretical background; numerical techniques; interaction with the program; computer experiments; real experiments and empirical evidence; reference.
Business Ethics of Innovation
Firms that operate in a market economy often depend upon innovations in order to achieve competitive advantages that sustainably secure their survival. Business ethics is thus largely concerned with questions about the decisional freedoms involved in innovation processes. Innovations oftentimes raise novel questions about the role of the state or the structure of society. Business ethics needs to provide a framework for balancing the different perspectives, values, and interests at stake. This balance must be achieved at the level of the firm in order to facilitate adequate long term decisions, but it should also be sought at higher, including regulatory, levels. Achieving this balance will require an ethical framework for entrepreneurial action.
