Optimization

Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering.

This book will serve as a reference, presenting state-of-the-art research on theoretical aspects of optimal sensor coverage problems.

This textbook provides an introduction to the use and understanding of optimization and modeling for upper-level undergraduate students in engineering and mathematics.

This textbook provides a hands-on treatment of the subject of optimization.

This thesis deals with an optimization problem from the field of theoretical plasma physics.

Estimation and Inference in Discrete Event Systems chooses a popular model for emerging automation systems-finite automata under partial observation-and focuses on a comprehensive study of the key problems of state estimation and event inference.

Optical networks epitomize complex communication systems, and they comprise the Internet's infrastructural backbone.

This book presents new techniques and methods for distributed control and optimization of networked microgrids.

Recent years have witnessed a surge of activity in the field of dynamic both theory and applications.

The book provides a broad introduction to both the theory and the application of optimization with a special emphasis on the elegance, importance, and usefulness of the parametric self-dual simplex method.

This book is devoted to the development of geometrie methods for studying and revealing geometrie aspects of the theory of differential equations with quadratie right-hand sides (Riccati-type equations), which are closely related to the calculus of variations and optimal control theory.

On March 15, 2002 we held a workshop on network interdiction and the more general problem of stochastic mixed integer programming at the University of California, Davis.

This book addresses remaining life prediction and predictive maintenance of equipment.

In today's world, with an increase in the breadth and scope of real-world engineering optimization problems as well as with the advent of big data, improving the performance and efficiency of algorithms for solving such problems has become an indispensable need for specialists and researchers.

Deep connections exist between harmonic and applied analysis and the diverse yet connected topics of machine learning, data analysis, and imaging science.

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The field of neural information processing has two main objects: investigation into the functioning of biological neural networks and use of artificial neural networks to sol ve real world problems.

Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications.

Biotechnology has been labelled as one of the key technologies of the last two decades of the 20th Century, offering boundless solutions to problems ranging from food and agricultural production to pharmaceutical and medical applications, as well as environmental and bioremediation problems.

Designed as a textbook for graduate courses on algorithms, this book presents efficient algorithms that find provably near-optimal solutions.

This volume collects research papers addressing topical issues in economics and management with a particular focus on dynamic models which allow to analyze and foster the decision making of firms in dynamic complex environments.

This thesis deals with an optimization problem from the field of theoretical plasma physics.

This book constitutes the refereed proceedings of the International Conference on Mathematical Modelling and Scientific Intelligence, ICMMSC 2012, Gandhigram, Tamil Nadu, India, in March 2012.

This monograph discusses the theoretical and practical development of multicriteria decision making (MCDM).

This book focuses on the design of efficient & dynamic methods to allocate divisible resources under various auction mechanisms, discussing their applications in power & microgrid systems and the V2G & EV charging coordination problems in smart grids.

A collection of essays on two different accounts of practical reason.

"e;This book will be a useful reference to control engineers and researchers.

The aim of the present book is to give a systematic treatment of the inverse problem of the calculus of variations, i.

Optimization problems arising in practice usually contain several random parameters.

It is commonly believed that macroeconomic models are not useful for policy analysis because they do not take proper account of agents' expectations.

This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science.

This book provides energy efficiency quantitative analysis and optimal methods for discrete manufacturing systems from the perspective of global optimization.

This is a comprehensive overview of the basics of fuzzy control, which also brings together some recent research results in soft computing, in particular fuzzy logic using genetic algorithms and neural networks.

Mathematics is the basis of casino games, which are the bedrock of a $100 billion/year industry.

The book provides an introduction to sub-Riemannian geometry and optimal transport and presents some of the recent progress in these two fields.

The area of adaptive systems, which encompasses recursive identification, adaptive control, filtering, and signal processing, has been one of the most active areas of the past decade.

Intended for advanced undergraduate/graduate students as well as scientists and engineers, this textbook presents a multi-disciplinary view of optimization, providing a thorough examination of algorithms, methods, techniques, and tools from diverse areas of optimization.

Das Lehrbuch stellt einen informationstheoretischen Zugang zur globalen Optimierung vor, der insbesondere die Behandlung hochdimensionaler Pobleme erlaubt.

This book features a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in B ethlehem, Pennsylvania, USA between August 16-18, 2017.

This unique book offers a unified approach to the modeling of rational decision-making under conditions of uncertainty and strategic and competitive interactions among agents.

Optimization is a rich and thriving mathematical discipline.

This two-volume book offers a comprehensive treatment of the probabilistic approach to mean field game models and their applications.

When elementary courses in discrete and combinatorial mathematics first became popular, they usually covered a broad spectrumofpure and applied topics.

This book is devoted to the study of classes of optimal control problems arising in economic growth theory, related to the Robinson-Solow-Srinivasan (RSS) model.

Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems.

Gathering the proceedings of the 12th CHAOS2019 International Conference, this book highlights recent developments in nonlinear, dynamical and complex systems.

Heinrich von Stackelberg's book, "e;Grundlagen einer reinen Kostentheorie"e;, published in 1932 was at the forefront of a growing conceptual revolution in the theory of the firm, a theory which appears more relevant than ever in today's economic climate.

This book provides a systematic and accessible approach to stochastic differential equations, backward stochastic differential equations, and their connection with partial differential equations, as well as the recent development of the fully nonlinear theory, including nonlinear expectation, second order backward stochastic differential equations, and path dependent partial differential equations.