Convexity and Optimization in Rn

This reference book gives the reader a complete but comprehensive presentation of the foundations of convex analysis and presents applications to significant situations in engineering. The presentation of the theory is self-contained and the proof of all the essential results is given. The examples consider meaningful situations such as the modeling of curvilinear structures, the motion of a mass of people or the solidification of a material. Non convex situations are considered by means of relaxation methods and the connections between probability and convexity are explored and exploited in order to generate numerical algorithms.

A systematic review of the most current decision models and techniques for disease prevention and treatment Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource of the most current decision models and techniques for disease prevention and treatment. With contributions from leading experts in the field, this important resource presents information on the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology. Designed to be accessible, in each chapter the text presents one decision problem with the related methodology to showcase the vast applicability of operations research tools and techniques in advancing medical decision making. This vital resource features the most recent and effective approaches to the quickly growing field of healthcare decision analytics, which involves cost-effectiveness analysis, stochastic modeling, and computer simulation. Throughout the book, the contributors discuss clinical applications of modeling and optimization techniques to assist medical decision making within complex environments. Accessible and authoritative, Decision Analytics and Optimization in Disease Prevention and Treatment: Presents summaries of the state-of-the-art research that has successfully utilized both decision analytics and optimization tools within healthcare operations research Highlights the optimization of chronic disease prevention, infectious disease control and prevention, and disease treatment and treatment technology Includes contributions by well-known experts from operations researchers to clinical researchers, and from data scientists to public health administrators Offers clarification on common misunderstandings and misnomers while shedding light on new approaches in this growing area Designed for use by academics, practitioners, and researchers, Decision Analytics and Optimization in Disease Prevention and Treatment offers a comprehensive resource for accessing the power of decision analytics and optimization tools within healthcare operations research.

Analog CMOS integrated circuits are in widespread use for communications, entertainment, multimedia, biomedical, and many other applications that interface with the physical world. Although analog CMOS design is greatly complicated by the design choices of drain current, channel width, and channel length present for every MOS device in a circuit, these design choices afford significant opportunities for optimizing circuit performance. This book addresses tradeoffs and optimization of device and circuit performance for selections of the drain current, inversion coefficient, and channel length, where channel width is implicitly considered. The inversion coefficient is used as a technology independent measure of MOS inversion that permits design freely in weak, moderate, and strong inversion. This book details the significant performance tradeoffs available in analog CMOS design and guides the designer towards optimum design by describing: An interpretation of MOS modeling for the analog designer, motivated by the EKV MOS model, using tabulated hand expressions and figures that give performance and tradeoffs for the design choices of drain current, inversion coefficient, and channel length; performance includes effective gate-source bias and drain-source saturation voltages, transconductance efficiency, transconductance distortion, normalized drain-source conductance, capacitances, gain and bandwidth measures, thermal and flicker noise, mismatch, and gate and drain leakage current Measured data that validates the inclusion of important small-geometry effects like velocity saturation, vertical-field mobility reduction, drain-induced barrier lowering, and inversion-level increases in gate-referred, flicker noise voltage In-depth treatment of moderate inversion, which offers low bias compliance voltages, high transconductance efficiency, and good immunity to velocity saturation effects for circuits designed in modern, low-voltage processes Fabricated design examples that include operational transconductance amplifiers optimized for various tradeoffs in DC and AC performance, and micropower, low-noise preamplifiers optimized for minimum thermal and flicker noise A design spreadsheet, available at the book web site, that facilitates rapid, optimum design of MOS devices and circuits Tradeoffs and Optimization in Analog CMOS Design is the first book dedicated to this important topic. It will help practicing analog circuit designers and advanced students of electrical engineering build design intuition, rapidly optimize circuit performance during initial design, and minimize trial-and-error circuit simulations.

Unique in that it focuses on formulation and case studies rather than solutions procedures covering applications for pure, generalized and integer networks, equivalent formulations plus successful techniques of network models. Every chapter contains a simple model which is expanded to handle more complicated developments, a synopsis of existing applications, one or more case studies, at least 20 exercises and invaluable references. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

This unique treatment systematically interprets a spectrum of importance measures to provide a comprehensive overview of their applications in the areas of reliability, network, risk, mathematical programming, and optimization. Investigating the precise relationships among various importance measures, it describes how they are modelled and combined with other design tools to allow users to solve readily many real-world, large-scale decision-making problems. Presenting the state-of-the-art in network analysis, multistate systems, and application in modern systems, this book offers a clear and complete introduction to the topic. Through describing the reliability importance and the fundamentals, it covers advanced topics such as signature of coherent systems, multi-linear functions, and new interpretation of the mathematical programming problems. Key highlights: Generalizes the concepts behind importance measures (such as sensitivity and perturbation analysis, uncertainty analysis, mathematical programming, network designs), enabling readers to address large-scale problems within various fields effectively Covers a large range of importance measures, including those in binary coherent systems, binary monotone systems, multistate systems, continuum systems, repairable systems, as well as importance measures of pairs and groups of components Demonstrates numerical and practical applications of importance measures and the related methodologies, including risk analysis in nuclear power plants, cloud computing, software reliability and more Provides thorough comparisons, examples and case studies on relations of different importance measures, with conclusive results based on the authors’ own research Describes reliability design such as redundancy allocation, system upgrading and component assignment. This book will benefit researchers and practitioners interested in systems design, reliability, risk and optimization, statistics, maintenance, prognostics and operations. Readers can develop feasible approaches to solving various open-ended problems in their research and practical work. Software developers, IT analysts and reliability and safety engineers in nuclear, telecommunications, offshore and civil industries will also find the book useful.

Discover the practical impacts of current methods of optimization with this approachable, one-stop resource Linear and Convex Optimization: A Mathematical Approach delivers a concise and unified treatment of optimization with a focus on developing insights in problem structure, modeling, and algorithms. Convex optimization problems are covered in detail because of their many applications and the fast algorithms that have been developed to solve them.  Experienced researcher and undergraduate teacher Mike Veatch presents the main algorithms used in linear, integer, and convex optimization in a mathematical style with an emphasis on what makes a class of problems practically solvable and developing insight into algorithms geometrically. Principles of algorithm design and the speed of algorithms are discussed in detail, requiring no background in algorithms. The book offers a breadth of recent applications to demonstrate the many areas in which optimization is successfully and frequently used, while the process of formulating optimization problems is addressed throughout.  Linear and Convex Optimization contains a wide variety of features, including: Coverage of current methods in optimization in a style and level that remains appealing and accessible for mathematically trained undergraduates Enhanced insights into a few algorithms, instead of presenting many algorithms in cursory fashion An emphasis on the formulation of large, data-driven optimization problems Inclusion of linear, integer, and convex optimization, covering many practically solvable problems using algorithms that share many of the same concepts Presentation of a broad range of applications to fields like online marketing, disaster response, humanitarian development, public sector planning, health delivery, manufacturing, and supply chain management Ideal for upper level undergraduate mathematics majors with an interest in practical applications of mathematics, this book will also appeal to business, economics, computer science, and operations research majors with at least two years of mathematics training.

The purpose of this book is to present the main methods of static and dynamic optimization. It has been written within the framework of the European Union project – ERRIC (Empowering Romanian Research on Intelligent Information Technologies), funded by the EU’s FP7 Research Potential program and developed in cooperation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the interested reader to explore various methods of implementation such as linear programming, nonlinear programming – particularly important given the wide variety of existing algorithms, dynamic programming with various application examples and Hopfield networks. The book examines optimization in relation to systems identification; optimization of dynamic systems with particular application to process control; optimization of large scale and complex systems; optimization and information systems.

Machine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.

Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU’s FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods of implementation such as metaheuristics, local search and populationbased methods. It examines multi-objective and stochastic optimization, as well as methods and tools for computer-aided decision-making and simulation for decision-making.