凸编程中的错位双曲增强拉格朗日算法

An International Journal of Optimization and Control: Theories & Applications (IJOCTA) Pub Date : 2024-04-07 DOI:10.11121/ijocta.1402

Lennin Mallma Ramirez, Nelson Maculan, Adilson Elias Xavier, Vinicius Layter Xavier

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引用次数: 0

摘要

错位双曲增强拉格朗日算法（DHALA）是双曲增强拉格朗日算法（HALA）的一种新方法。DHALA 专为解决凸非线性编程问题而设计。我们保证 DHALA 生成的序列收敛于 Karush-Kuhn-Tucker 点。我们将观察到，与 HALA 相比，DHALA 在求解问题时略有计算优势。最后，我们将通过计算来说明我们的理论结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Dislocation hyperbolic augmented Lagrangian algorithm in convex programming

The dislocation hyperbolic augmented Lagrangian algorithm (DHALA) is a new approach to the hyperbolic augmented Lagrangian algorithm (HALA). DHALA is designed to solve convex nonlinear programming problems. We guarantee that the sequence generated by DHALA converges towards a Karush-Kuhn-Tucker point. We are going to observe that DHALA has a slight computational advantage in solving the problems over HALA. Finally, we will computationally illustrate our theoretical results.

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An International Journal of Optimization and Control: Theories & Applications (IJOCTA)

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