一种光谱的Fletcher-Reeves相结合的梯度方法与为不受约束的优化和投资组合选择的综合战略
Nasiru Salihu1, Sulaiman M Ibrahim2,3, P Kaelo4
1Department of Mathematics, Faculty of Sciences, Modibbo Adama University, Yola, Nigeria.
PloS one
|April 25, 2025
概括
一种新的结构化光谱合梯度 (SCG) 方法改善了不受约束的优化和投资组合选择. 这种高效的算法提高了计算性能,并为复杂的问题实现了更好的优化结果.
科学领域:
- 优化理论 优化理论
- 数字分析 数字分析
- 计算金融是指计算金融.
背景情况:
- 大规模的不受约束的优化问题带来了重大的计算挑战.
- 现有的光谱合梯度 (SCG) 技术提供了效率,但可以进一步改进.
- 投资组合选择需要强大的方法来优化回报和最小化风险.
研究的目的:
- 引入一种新的结构化光谱合梯度 (SCG) 算法.
- 增强联梯度 (CG) 方法的总体结构和性能.
- 在投资组合选择中应用和评估扩展的SCG方法用于股票配置.
主要方法:
- 开发了一个结构化的SCG方法,整合了准牛顿方向和扩展的结合条件.
- 纳入了Fletcher-Reeves相结合梯度参数以进行结构改进.
- 建立了使用沃尔夫线搜索标准对一般函数的全球收.
主要成果:
- 数字实验证明了SCG算法的优越性,超过现有的CG方法.
- 扩展的SCG方法显示了计算效率的显著提高,用于不受约束的优化.
- 投资组合选择的应用产生了优化的股票配置,降低了风险和提高了回报.
结论:
- 结构化SCG方法为大规模不受约束的优化提供了强大而高效的解决方案.
- 该算法为金融应用提供了有价值的工具,特别是在投资组合优化方面.
- 经验评估证实了该方法在提高计算效率和优化结果方面的有效性.
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