Lp 准规范最小化:算法和应用
Omar M Sleem1, M E Ashour2, N S Aybat3
1Department of Electrical Engineering, Pennsylvania State University, State College, PA, 16802 USA.
本研究引入了一种新的启发式方法,通过最小化Lq准规范 (q<1) 来解决优化问题. 新的算法为各种应用提供了高效的稀疏解决方案,优于现有的Lq-规范最小化技术.
科学领域:
- 优化优化 优化优化
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 在统计,机器学习和信号处理中, Sparsity 对于高效的计算和存储至关重要.
- 尽量减少Lq准规范 (q<1) 是实现优化问题的稀疏解决方案的关键目标.
结论:
- 建议的启发式方法为稀疏优化提供了一种高效的方法.
- 开发的算法比现有的Lq准规范最小化技术显著提高了性能.
- 这项工作推进了稀疏恢复和相关的计算领域.
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