Iterative dynamic programming

R. Luus
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引用次数: 89

Abstract

From the Publisher: Dynamic programming is a powerful method for solving optimizationproblems, but has a number of drawbacks that limit its use to solving problems of very low dimension. To overcome these limitations, author Rein Luus suggested using it in an iterative fashion. Although this method required vast computer resources, modifications to his original scheme have made the computational procedure feasible. With iteration, dynamic programming becomes an effective optimization procedure for very high-dimensional optimal control problems and has demonstrated applicability to singular control problems. Recently, iterative dynamic programming (IDP) has been refined to handle inequality state constraints and noncontinuous functions. Iterative Dynamic Programming offers the first comprehensive presentation of this powerful tool. It brings together the results of work carried out by the author and others - previously available only in scattered journal articles - along with the insight that led to its development. The author provides the necessary background, examines the effects of the parameters involved, and clearly illustrates IDP's advantages.
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迭代动态规划
动态规划是解决优化问题的一种强大的方法,但是有一些缺点限制了它在解决低维问题中的使用。为了克服这些限制,作者Rein Luus建议以迭代的方式使用它。虽然这种方法需要大量的计算机资源,但对他的原始方案的修改使计算过程变得可行。通过迭代,动态规划成为求解高维最优控制问题的一种有效的优化方法,并证明了它对奇异控制问题的适用性。近年来,迭代动态规划(IDP)得到了改进,可用于处理不等式状态约束和不连续函数。迭代动态规划提供了这个强大工具的第一个全面的介绍。它汇集了作者和其他人的工作成果——以前只能在零散的期刊文章中获得——以及导致其发展的洞察力。作者提供了必要的背景,考察了所涉及的参数的影响,并清楚地说明了IDP的优点。
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Iterative dynamic programming State constraints Fundamental concepts Sensitivity considerations Nonseparable problems
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