基于梯度的高效算法,下降方向为不受约束的优化,可用于图像恢复和机器人运动控制
Sulaiman Mohammed Ibrahim1,2, Aliyu M Awwal3, Maulana Malik4
1School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah, Malaysia.
PeerJ. Computer science
|June 26, 2025
概括
一个新的渐变算法改进了图像恢复和机器人控制的优化模型. 这种修改后的合梯度方法确保了稳定性,并在实验中展示了卓越的性能.
科学领域:
- 优化算法 优化算法
- 计算机科学应用 计算机科学应用
- 应用数学 应用数学 应用数学
背景情况:
- 优化模型对于复杂的任务至关重要,如图像恢复和机器人控制.
- 现有的基于梯度的方法在保持下降特性和确保全球收方面面临挑战.
- 结合梯度 (CG) 方法是一种广泛使用的优化技术.
研究的目的:
- 引入一种新的基于梯度的算法,以提高优化模型的性能.
- 为了确保修改后的结合梯度 (CG) 系数保持搜索方向的下降属性.
- 在特定条件下建立拟议的算法的全球收.
主要方法:
- 开发一个修改的结合梯度 (CG) 算法.
- 确保CG系数 (β κ) 整合到搜索方向.
- 使用强烈的沃尔夫条件,并假设利普希茨连续性用于收分析.
主要成果:
- 在适当的线路搜索条件下,算法保持了下降属性.
- 拟议的算法的全球融合理论上已经确立.
- 计算实验显示在图像恢复和机器人运动控制 (3DOF手臂) 中的性能优越.
结论:
- 新的基于梯度的算法为优化模型提供了增强的性能.
- 该方法显示了在图像处理和机器人技术中的实际应用的巨大潜力.
- 已建立的全球融合为算法的可靠性提供了坚实的理论基础.
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