Dislocation hyperbolic augmented Lagrangian algorithm in convex programming

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

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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凸编程中的错位双曲增强拉格朗日算法
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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