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الصفحة 7
الصفحة 7
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.
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.
Leray–Schauder Type Alternatives, Complementarity Problems and Variational Inequalities
Complementarity theory, a relatively new domain in applied mathematics, has deep connections with several aspects of fundamental mathematics and also has many applications in optimization, economics and engineering. The study of variational inequalities is another domain of applied mathematics with many applications to the study of certain problems with unilateral conditions. This book is the first to discuss complementarity theory and variational inequalities using Leray–Schauder type alternatives.
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.
Laplacian Eigenvectors of Graphs : Perron-Frobenius and Faber-Krahn Type Theorems
Eigenvectors of graph Laplacians have not, to date, been the subject of expository articles and thus they may seem a surprising topic for a book. The authors propose two motivations for this new LNM volume: (1) There are fascinating subtle differences between the properties of solutions of Schrödinger equations on manifolds on the one hand, and their discrete analogs on graphs. (2) "Geometric" properties of (cost) functions defined on the vertex sets of graphs are of practical interest for heuristic optimization algorithms. The observation that the cost functions of quite a few of the well-studied combinatorial optimization problems are eigenvectors of associated graph Laplacians has prompted the investigation of such eigenvectors.
Lagrangian Probability Distributions
Lagrangian expansions can be used to obtain numerous useful probability models, which have been applied to real life situations including, but not limited to: branching processes, queuing processes, stochastic processes, environmental toxicology, diffusion of information, ecology, strikes in industries, sales of new products, and production targets for optimum profits. This book presents a comprehensive, systematic treatment of the class of Lagrangian probability distributions, along with some of its families, their properties, and important applications.
Lagrangian and Hamiltonian Methods for Nonlinear Control 2006 ; Proceedings from the 3rd IFAC Workshop, Nagoya, Japan, July 2006
A Differential-Geometric Approach for Bernstein’s Degrees-of-Freedom Problem.- Nonsmooth Riemannian Optimization with Applications to Sphere Packing and Grasping.- Synchronization of Networked Lagrangian Systems.- An Algorithm to Discretize One-Dimensional Distributed Port Hamiltonian Systems.- Virtual Lagrangian Construction Method for Infinitedimensional Systems with Homotopy Operators.- Direct Discrete-Time Design for Sampled-Data Hamiltonian Control Systems.- Kinematic Compensation in Port-Hamiltonian Telemanipulation.- Interconnection and Damping Assignment Passivity-Based Control of a Four-Tank System.- Towards Power-based Control Strategies for a Class of Nonlinear Mechanical Systems.- Power Shaping Control of Nonlinear Systems: A Benchmark Example.- Total Energy Shaping Control of Mechanical Systems: Simplifying the Matching Equations via Coordinate Changes.- Simultaneous Interconnection and Damping Assignment Passivity–Based Control: Two Practical Examples.
Knowledge-Driven Computing : Knowledge Engineering and Intelligent Computations
Knowledge-Driven Computing constitutes an emerging area of intensive research located at the intersection of Computational Intelligence and Knowledge Engineering with strong mathematical foundations. It embraces methods and approaches coming from diverse computational paradigms, such as evolutionary computation and nature-inspired algorithms, logic programming and constraint programming, rule-based systems, fuzzy sets and many others. The use of various knowledge representation formalisms and knowledge processing and computing paradigms is oriented towards the efficient resolution of computationally complex and difficult problems.
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.
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.
Composite Systems Decisions
Composite decisions consist of interconnected parts or subdecisions and correspond to a composite (composable, modular, decomposable) system. Composite Systems Decisions describes an educational approach that is based on systems engineering and considered modular design of composite decisions. Divided into four parts, this book contains descriptions of basic systems approaches and examines basic ‘technological’ problems for composite systems, including: modular hierarchical design; multistage design; multistage planning; redesign/improvement/adaptation; evaluation; and, combinatorial evolution/development.
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.
Complex decision making : Theory and practice
The increasingly complex environment of today's world, characterized by technological innovation and global communication, generates myriads of possible and actual interactions while limited physical and intellectual resources severely impinge on decision makers, be it in the public or private domains. At the core of the decision-making process is the need for quality information that allows the decision maker to better assess the impact of decisions in terms of outcomes, nonlinear feedback processes and time delays on the performance of the complex system invoked.
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.
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.
Challenges for Computational Intelligence
Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.
CDMA Radio with Repeaters
This book analyzes the interaction of the CDMA system within a mobile channel environment, emphasizing repeaters, diversities, and other performance-enhancing means. The authors provide a unique and clear description of repeater-embedded networks’ planning, design, and optimization in different scenarios. Simple physically-based expressions are developed and supported by extensive field experience, and provide insight into the network dynamics. CDMA Radio with Repeaters is intended for wireless RF engineers and system architects, and for researchers working on advanced wireless networks, including MIMO systems. The material also provides insight for helping cellular managers and business assess costs and risks. This book is also suitable as a reference book for graduate courses.
CCD Image Sensors in Deep-Ultraviolet : Degradation Behavior and Damage Mechanisms
As the deep-ultraviolet (DUV) laser technology continues to mature, an increasing number of industrial and manufacturing applications are emerging. For example, the new generation of semiconductor inspection systems is being pushed to image at increasingly shorter DUV wavelengths to facilitate inspection of deep sub-micron features in integrated circuits. DUV-sensitive charge-coupled device (CCD) cameras are in demand for these applications. Although CCD cameras that are responsive at DUV wavelengths are now available, their long-term stability is still a major concern. This book describes the degradation mechanisms and long-term performance of CCDs in the DUV, along with new results of device performance at these wavelengths.
