No-regret algorithms for online k-submodular maximization

07/13/2018

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by   Tasuku Soma, et al.

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We present a polynomial time algorithm for online maximization of k-submodular maximization. For online (nonmonotone) k-submodular maximization, our algorithm achieves a tight approximate factor in an approximate regret. For online monotone k-submodular maximization, our approximate-regret matches to the best-known approximation ratio, which is tight asymptotically as k tends to infinity. Our approach is based on the Blackwell approachability theorem and online linear optimization.