一个加速的最小化算法,用于凸-凸的座点问题,具有非光滑的合函数
Radu Ioan Boţ1,2, Ernö Robert Csetnek1, Michael Sedlmayer2
1Faculty of Mathematics, University of Vienna, Vienna, Austria.
概括
这项研究介绍了OGAProx,这是一种用于用非光滑元件解决凸空式坐点问题的新算法. 它在各种场景中实现了代和函数值的改进趋同率.
科学领域:
- 优化理论 优化理论
- 凸的分析 凸的分析
- 机器学习算法 机器学习算法
背景情况:
- 凸凸凸的坐点问题是优化和游戏理论的基础.
- 现有的算法经常在合函数和调节器中与不平滑性作斗争.
- 对于机器学习等应用程序来说,有效地解决这些问题至关重要.
研究的目的:
- 开发和分析一种新的算法,OGAProx,用于一类非光滑的凸形座点问题.
- 在不同的凸度假设下调查算法的性能 (凸-,凸-强烈,强烈-强烈).
- 为代和函数值建立理论的收率.
主要方法:
- 拟议的OGAProx算法将光滑变量的乐观梯度上升与非光滑组件的近接步骤相结合.
- 分析了不同问题设置的收属性.
- 通过应用在非平滑线性问题,多核SVM培训和minimax组公平性分类中验证了理论发现.
主要成果:
- 实现了 (弱) 代的收.
- 对于代的确定的收率为O(1/K) 和线性收率为O(θ^K).
- 证明了函数值的O(1/K),O(1/K^2和O(θ^K) 的厄尔戈迪克收率.
结论:
- OGAProx是一个有效的算法,用于解决非光滑的凸凸的角问题.
- 理论上的趋同保证得到了实际应用的验证.
- 该算法在需要点优化的各种机器学习任务中表现有前途.
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