We address the problem of constructing randomized online algorithms for the Metrical Task Systems (MTS) problem on a metric δ against an oblivious adversary. Restricting our attention to the class of "work-based" algorithms, we provide a framework for designing algorithms that uses the technique of regularization. For the case when δ is a uniform metric, we exhibit two algorithms that arise from this framework, and we prove a bound on the competitive ratio of each. We show that the second of these algorithms is ln n + O(log log n) competitive, which is the current state-of-the art for the uniform MTS problem.
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机译：我们解决了对一个令人沮丧的对手的度量δ上的标准任务系统（MTS）问题构建随机在线算法的问题。限制我们对“基于工作”算法的类别，我们为设计使用正规化技术的算法提供了一个框架。对于δ是均匀度量的情况，我们展示了从该框架出现的两种算法，并且我们证明了每个竞争比例的绑定。我们表明，这些算法中的第二个是LN N + O（log log n）竞争力，这是统一MTS问题的当前最先进的。
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