Integrated methods for optimization
Integrated methods for optimization
复制标题
综合优化方法
DOI：
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发表时间：
2011
期刊：
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通讯作者：
J. Hooker
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作者：
J. Hooker
《优化综合方法》第一版于 2007 年 1 月出版。由于该书涵盖了一个快速发展的领域，因此现在正是出版第二版的最佳时机。本书提供了统一的处理优化方法。它将数学规划 (MP)、约束规划 (CP) 和全局优化 (GO) 的思想集中到一本书中。没有理由必须像通常那样将它们作为单独的领域来学习，并且有三个原因应该将它们一起研究。 (1) 它们在智力上有很多共同点，在很大程度上可以将它们理解为单一底层解决方案技术的特例。 (2) 越来越多的文献报道了如何将它们整合起来以有利地制定和解决广泛的问题。 (3) 现在有几个软件包包含了这些领域中两个或多个领域的技术。本书为希望在单一学习课程中获得优化方法全面背景的研究生和从业者提供了独特的资源。工程专业的学生是一个特别大的潜在受众，因为工程优化问题通常受益于组合方法，特别是在涉及设计、调度或物流的情况下。本书对那些学习运筹学的人也很有价值，因为他们的教育课程很少涉及 CP，对那些学习计算机科学和人工智能 (AI) 的人也很有价值，因为他们的课程通常省略 MP 和 GO。对于这些领域中想要了解另一个领域的从业者来说，该文本也很有用，因为它提供了比其他文本更简洁、更易于理解的处理方法。这本书可以涵盖如此广泛的材料，因为它重点关注与通用优化和约束求解器中使用的方法相关的想法。本书重点介绍了已被证明在通用优化和约束求解器以及当前和可预见的未来的集成求解器中有用的方法背后的想法。第二版更新了该领域的结果，包括几个主要的新主题：线性、非线性和动态规划的背景材料。网络流理论，因为它在过滤算法中的重要性。关于广义对偶理论的一章，更明确地为优化方法开发了统一的原对偶算法结构。使用原对偶框架作为组织原则，对 MP 和 AI 的搜索方法进行了广泛的调查。涵盖了 CP 求解器中使用的几个附加全局约束。本书继续关注精确与相反启发式方法。可以将启发式方法引入书中描述的统一方案中，新版本将保留对如何实现这一点的简短讨论。
The first edition of Integrated Methods for Optimization was published in January 2007. Because the book covers a rapidly developing field, the time is right for a second edition. The book provides a unified treatment of optimization methods. It brings ideas from mathematical programming (MP), constraint programming (CP), and global optimization (GO)into a single volume. There is no reason these must be learned as separate fields, as they normally are, and there are three reasons they should be studied together. (1) There is much in common among them intellectually, and to a large degree they can be understood as special cases of a single underlying solution technology. (2) A growing literature reports how they can be profitably integrated to formulate and solve a wide range of problems. (3) Several software packages now incorporate techniques from two or more of these fields. The book provides a unique resource for graduate students and practitioners who want a well-rounded background in optimization methods within a single course of study. Engineering students are a particularly large potential audience, because engineering optimization problems often benefit from a combined approachparticularly where design, scheduling, or logistics are involved. The text is also of value to those studying operations research, because their educational programs rarely cover CP, and to those studying computer science and artificial intelligence (AI), because their curricula typically omit MP and GO. The text is also useful for practitioners in any of these areas who want to learn about another, because it provides a more concise and accessible treatment than other texts. The book can cover so wide a range of material because it focuses on ideas that arerelevant to the methods used in general-purpose optimization and constraint solvers. The book focuses on ideas behind the methods that have proved useful in general-purpose optimization and constraint solvers, as well as integrated solvers of the present and foreseeable future. The second edition updates results in this area and includes several major new topics: Background material in linear, nonlinear, and dynamic programming.Network flow theory, due to its importance in filtering algorithms.A chapter on generalized duality theory that more explicitly develops a unifying primal-dual algorithmic structure for optimization methods.An extensive survey of search methods from both MP and AI, using the primal-dual framework as an organizing principle.Coverage of several additional global constraints used in CP solvers.The book continues to focus on exact as opposed to heuristic methods. It is possible to bring heuristic methods into the unifying scheme described in the book, and the new edition will retain the brief discussion of how this might be done.