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Theory of Multiobjective Optimization

Theory of Multiobjective Optimization,多目标优化理论
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Nonlinear Multiobjective Optimization

K. Miettinen, Nonlinear Multiobjective Optimization. Norwell, A:Kluwer, 1999. Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.

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Multi-Objective Optimization using Evolutionary Algorithms

Multi-Objective Optimization using Evolutionary Algorithms,本书介绍了利用进化算法解决多目标优化问题,包括基础知识,应用等等。

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Topology optimization-Theory, Methods and Applications

Topology optimization-Theory, Methods and Applications

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multiobjective optimization

multiobjective optimization

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Engineering Optimization: Theory and Practice 4th

Helps you move from theory to optimizing engineering systems in almost any industry Now in its Fourth Edition, Professor Singiresu Rao's acclaimed text Engineering Optimization enables readers to quickly master and apply all the important optimization methods in use today across a broad range of industries. Covering both the latest and classical optimization methods, the text starts off with the basics and then progressively builds to advanced principles and applications. This comprehensive text covers nonlinear, linear, geometric, dynamic, and stochastic programming techniques as well as more specialized methods such as multiobjective, genetic algorithms, simulated annealing, neural networks, particle swarm optimization, ant colony optimization, and fuzzy optimization. Each method is presented in clear, straightforward language, making even the more sophisticated techniques easy to grasp. Moreover, the author provides: * Case examples that show how each method is applied to solve real-world problems across a variety of industries * Review questions and problems at the end of each chapter to engage readers in applying their newfound skills and knowledge * Examples that demonstrate the use of MATLAB® for the solution of different types of practical optimization problems * References and bibliography at the end of each chapter for exploring topics in greater depth * Answers to Review Questions available on the author's Web site to help readers to test their understanding of the basic concepts With its emphasis on problem-solving and applications, Engineering Optimization is ideal for upper-level undergraduates and graduate students in mechanical, civil, electrical, chemical, and aerospace engineering. In addition, the text helps practicing engineers in almost any industry design improved, more efficient systems at less cost.

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Deep Belief Networks Ensemble

Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics .pdf

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Optimization Theory and Methods_Nonlinear Programming

从springer上找到的,Nonlinear optimization的常见方法很全面哦:)

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Combinatorial-Optimization-Theory-and-Algorithm

这是关于组合优化算法的电子书,高清,最新版本,经典著作,英文版

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convex optimization高清PDF版

这本书主要是面向实际应用。书中提供了凸优化的理论框架,但不强调复杂的定理证明。丰富的实例是这本书的特色。实例涉及的领域非常广例如通信,金融,机器学习等等。

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Convex Optimization pdf

凸优化,有助于理解SVM中的对偶问题,证明位于Page-234, 5.3.2节

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《Theory of Convex Optimization for Machine Learning》2015版

《Convex Optimization - Algorithms and Complexity》(2015 Sébastien Bubeck)即先前《Theory of Convex Optimization for Machine Learning》的升级版

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convex optimization theory_bersekas

经典书籍不用多介绍,优化大师BertSekas经典力作,凸优化理论,包含最新版的第6章算法部分

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Non-convex Optimization for Machine Learning.pdf

机器学习中,常用优化算法,采用的是凸函数优化。随着深度学习的发展,在深度学习等训练是,经常涉及到非凸函数优化问题,该书做了相关的研究和说明。

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NSGA 多目标遗传算法优化方法(C)

Multiobjective optimization using nondominated sorting genetic algorithms

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Engineering Optimization: Theory and Practice, 3rd Edition

A rigorous mathematical approach to identifying a set of design alternatives and selecting the best candidate from within that set, engineering optimization was developed as a means of helping engineers to design systems that are both more efficient and less expensive and to develop new ways of improving the performance of existing systems. Thanks to the breathtaking growth in computer technology that has occurred over the past decade, optimization techniques can now be used to find creative solutions to larger, more complex problems than ever before. As a consequence, optimization is now viewed as an indispensable tool of the trade for engineers working in many different industries, especially the aerospace, automotive, chemical, electrical, and manufacturing industries.<br><br>In Engineering Optimization, Professor Singiresu S. Rao provides an application-oriented presentation of the full array of classical and newly developed optimization techniques now being used by engineers in a wide range of industries. Essential proofs and explanations of the various techniques are given in a straightforward, user-friendly manner, and each method is copiously illustrated with real-world examples that demonstrate how to maximize desired benefits while minimizing negative aspects of project design.<br><br>Comprehensive, authoritative, up-to-date, Engineering Optimization provides in-depth coverage of linear and nonlinear programming, dynamic programming, integer programming, and stochastic programming techniques as well as several breakthrough methods, including genetic algorithms, simulated annealing, and neural network-based and fuzzy optimization techniques.<br><br>Designed to function equally well as either a professional reference or a graduate-level text, Engineering Optimization features many solved problems taken from several engineering fields, as well as review questions, important figures, and helpful references.<br><br>An indispensable working resource for practicing engineers<br><br>Engineering Optimization<br><br>Providing engineers with a rigorous, systematic method for rapidly zeroing in on the most innovative, cost-effective solutions to some of today\'s most challenging engineering design problems, optimization is a powerful tool of the trade for engineers in virtually every discipline. Now, in his latest book, Engineering Optimization, Singiresu S. Rao provides you with the most practical, up-to-date, and comprehensive coverage of new and classical optimization techniques currently in use throughout a wide range of industries. Designed to serve as both a daily working resource and an excellent graduate-level text, Engineering Optimization gives you: <br>* In-depth coverage of linear and nonlinear programming, dynamic programming, integer programming, and stochastic programming techniques <br>* New or recently developed methods, including genetic algorithms, simulated annealing, neural network-based and fuzzy optimization techniques <br>* Dozens of real-world design optimization examples taken from a wide range of industries <br>* Numerous solved problems and review questions <br>* An extensive bibliography<br><br><br>Engineering Optimization is a valuable working resource for engineers employed in practically all technological industries. It is also a superior didactic tool for graduate students of mechanical, civil, electrical, chemical, and aerospace engineering. <br>

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Nonlinear Multiobjective Optimization(kaisa)

K. Miettinen, Nonlinear Multiobjective Optimization. Norwell, A:Kluwer, 1999. Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.

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OPTIMIZATION THEORY AND METHODS Nonlinear Programming

Optimization has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in all areas of applied mathematics, engineering, medicine, economics and other sciences.

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jMetal 4.3 for multiobjective optimization

基于java的多目标优化源程序jMetal 4.3最新版,包括NSGA-II\SPEA2\MOPSO等。-jMetal 4.3 for multiobjective optimization

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DIMITRI BERTSEKAS_Convex Optimization Theory_solutions

MIT DIMITRI BERTSEKAS教授的Convex Optimization Theory 课程PPT,教材详细摘要和教材习题解答等。

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Global Optimization Algorithms--Theory and Application

This e-book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems.

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