An optimal algorithm for bandit convex optimization
نویسندگان
چکیده
We consider the problem of online convex optimization against an arbitrary adversary with bandit feedback, known as bandit convex optimization. We give the first Õ( √ T )-regret algorithm for this setting based on a novel application of the ellipsoid method to online learning. This bound is known to be tight up to logarithmic factors. Our analysis introduces new tools in discrete convex geometry.
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ورودعنوان ژورنال:
- CoRR
دوره abs/1603.04350 شماره
صفحات -
تاریخ انتشار 2016