Hardness of Online Sleeping Combinatorial Optimization Problems

We show that several online combinatorial optimization problems that admit efficient no-regret algorithms become computationally hard in the sleeping setting where a subset of actions becomes unavailable in each round. Specifically, we show that the sleeping versions of these problems are at least as hard as PAC learning DNF expressions, a long standing open problem. We show hardness for the sleeping versions of ONLINE SHORTEST PATHS, ONLINE MINIMUM SPANNING TREE, ONLINE k-SUBSETS, ONLINE k-TRUNCATED PERMUTATIONS, ONLINE MINIMUM CUT, and ONLINE BIPARTITE MATCHING. The hardness result for the sleeping version of the Online Shortest Paths problem resolves an open problem presented at COLT 2015 [Koolen et al., 2015].