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Mathematical Optimization Terminology: A Comprehensive Glossary of Terms is a practical book with the essential formulations, illustrative examples, real-world applications and main references on the topic. This book helps readers gain a more practical understanding of optimization, enabling them to apply it to their algorithms. This book also addresses the need for a practical publication that introduces these concepts and techniques. Discusses real-world applications of optimization and how it can be used in algorithms Explains the essential formulations of optimization in mathematics Covers a more practical approach to optimization
Mathematical Optimization Terminology takes into account multi-objective method considering the problems related to optimization in case of power distribution systems. It includes a short survey on model-based evolutionary algorithms and study on parameter optimization for support vector regression. This book also discusses about gradient-based cuckoo search for global optimization, a hybrid cuckoo search algorithm, extraction methods for uncertain inference rules by ant colony optimization, a benchmark test suite for evolutionary many-objective optimization, a hybrid SA-MFO algorithm for function optimization and engineering design problems and social group optimization (SGO).
This volume contains a collection of 23 papers presented at the 4th French-German Conference on Optimization, hold at Irsee, April 21 - 26, 1986. The conference was aUended by ninety scientists: about one third from France, from Germany and from third countries each. They all contributed to a highly interesting and stimulating meeting. The scientifique program consisted of four survey lectures of a more tutorical character and of 61 contributed papers covering almost all areas of optimization. In addition two informal evening sessions and a plenary discussion on further developments of optimization theory were organized. One of the main aims of the organizers was to indicate and to stress the increasing importance of optimization methods for almost all areas of science and for a fast growing number of industry branches. We hope that the conference approached this goal in a certain degree and managed to continue fruitful discussions between -theory and -applications-. Equally important to the official contributions and lectures is the -nonmeasurable part of activities inherent in such a scientific meeting. Here the charming and inspiring atmosphere of a place like Irsee helped to establish numerous new contacts between the participants and to deepen already existing ones. The conference was sponsored by the Bayerische Kultusministerium, the Deutsche Forschungsgemeinschaft and the Universities of Augsburg and Bayreuth. Their interest in the meeting and their assistance is gratefully acknowledged. We would like to thank the authors for their contributions and the referees for their helpful comments.
Give Your Students the Proper Groundwork for Future Studies in Optimization A First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering. It teaches students the basics of continuous optimization and helps them better understand the mathematics from previous courses. The book focuses on general problems and the underlying theory. It introduces all the necessary mathematical tools and results. The text covers the fundamental problems of constrained and unconstrained optimization as well as linear and convex programming. It also presents basic iterative solution algorithms (such as gradient methods and the Newton–Raphson algorithm and its variants) and more general iterative optimization methods. This text builds the foundation to understand continuous optimization. It prepares students to study advanced topics found in the author’s companion book, Iterative Optimization in Inverse Problems, including sequential unconstrained iterative optimization methods.
This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.
This book is intended to be a teaching aid for students of the courses in Operations Research and Mathematical Optimization for scientific faculties. Some of the basic topics of Operations Research and Optimization are considered: Linear Programming, Integer Linear Programming, Computational Complexity, and Graph Theory. Particular emphasis is given to Integer Linear Programming, with an exposition of the most recent resolution techniques, and in particular of the branch-and-cut method. The work is accompanied by numerous examples and exercises.
During the last decade the techniques of non-linear optim ization have emerged as an important subject for study and research. The increasingly widespread application of optim ization has been stimulated by the availability of digital computers, and the necessity of using them in the investigation of large systems. This book is an introduction to non-linear methods of optimization and is suitable for undergraduate and post graduate courses in mathematics, the physical and social sciences, and engineering. The first half of the book covers the basic optimization techniques including linear search methods, steepest descent, least squares, and the Newton-Raphson method. These are described in detail, with worked numerical examples, since they form the basis from which advanced methods are derived. Since 1965 advanced methods of unconstrained and constrained optimization have been developed to utilise the computational power of the digital computer. The second half of the book describes fully important algorithms in current use such as variable metric methods for unconstrained problems and penalty function methods for constrained problems. Recent work, much of which has not yet been widely applied, is reviewed and compared with currently popular techniques under a few generic main headings. vi PREFACE Chapter I describes the optimization problem in mathemat ical form and defines the terminology used in the remainder of the book. Chapter 2 is concerned with single variable optimization. The main algorithms of both search and approximation methods are developed in detail since they are an essential part of many multi-variable methods.
Light will be thrown on a variety of problems concerned with the construction and analysis of optimization models: equilibrium models of mathematical economy, modern numerical optimization methods and software, methods of convex programming optimal with respect to complexity, polynomial algorithms of linear programming, decomposition of optimization systems, modern apparatus of nonsmooth optimization, models and methods of discrete programming.
Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.
Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.