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A Graph-based Method to Solve the Economical Dispatch Problem Disregarding Slack Variables

机译:基于图形的方法解决了忽视了松弛变量的经济派遣问题

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One of the greatest challenges to confront Nonlinear Programming Problems, it is the selection of the active and non active set of constraints of the system. For this reason many optimization applications prefer to use barrier or penalty methods with their related inefficiencies. This paper describes a graph-based solution for these models which facilitates the handling of such constraints and, therefore, the solution process for the model. To this end some parts of the graph are considered active or non active, depending on the actual model solution as well as the values of the Lagrange multipliers. At every solution step, there will probably be some changes on the graph topology to reflect the current conditions of the problem whose solution is in progress. These solutions besides being efficient, provide an optimal storage scheme as only the fundamental information of the problem is stored.
机译:面对非线性编程问题的最大挑战之一,它是选择系统的主动和非活动的约束集。因此,许多优化应用程序更喜欢使用相关的估量障碍或惩罚方法。本文介绍了基于图形的解决方案,用于这些模型促进了这种约束的处理,因此是该模型的解决方案过程。为此,图形的某些部分被视为有效或非活动,具体取决于实际模型解决方案以及拉格朗日乘法器的值。在每个解决方案步骤中,图形拓扑可能会有一些变化,以反映解决方案正在进行的问题的当前条件。除了高效之外,这些解决方案还提供了最佳存储方案,仅作为存储问题的基本信息。

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