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两个衡量标准的故事:竞争力和遗憾的同时发生

A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret
课程网址: http://videolectures.net/colt2013_barman_bounds/  
主讲教师: Siddharth Barman
开课单位: 加州理工学院
开课时间: 2013-08-09
课程语种: 英语
中文简介:
我们考虑“平滑在线凸优化”问题的算法,这是与测量任务系统密切相关的在线凸优化问题类的变体。 关于这些问题的先前文献集中在两个绩效指标:遗憾和竞争比率。 存在已知的具有次线性遗憾的算法和具有恒定竞争比的已知算法; 然而,没有已知算法同时实现两者。 我们证明这是由于这两个指标之间的基本不相容性 - 即使在目标函数是线性的情况下,也没有算法(确定性或随机化)可以实现次线性遗憾和恒定竞争比率。 然而,我们还展示了一种算法,对于一维决策空间的重要特殊情况,它提供了次线性后悔,同时保持了任意缓慢增长的竞争比率。
课程简介: We consider algorithms for “smoothed online convex optimization” problems, a variant of the class of online convex optimization problems that is strongly related to metrical task systems. Prior literature on these problems has focused on two performance metrics: regret and the competitive ratio. There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however, no known algorithm achieves both simultaneously. We show that this is due to a fundamental incompatibility between these two metrics - no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear. However, we also exhibit an algorithm that, for the important special case of one dimensional decision spaces, provides sublinear regret while maintaining a competitive ratio that grows arbitrarily slowly.
关 键 词: 在线凸优化; 竞争比率; 数学算法; 数学分析; 线性优化
课程来源: 视频讲座网公开课
最后编审: 2019-05-26:cwx
阅读次数: 69