The kk-partite planted clique conjecture

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Fix a constant p∈(0,1)p\in(0,1). Let ntn_t and ktk_t be increasing sequences of positive integers such that kt=o(nt)k_t=o(\sqrt{n_t}) and ktk_t divides ntn_t, and let

At:Gnt→{0,1}\mathcal{A}_t:\mathcal{G}_{n_t}\to\{0,1\}

be a sequence of randomized polynomial-time algorithms. Under H0H_0, GG is sampled from the null distribution, while under H1H_1 it is sampled from the ktk_t-partite planted-clique distribution k-pc(nt,kt,p)k\text{-pc}(n_t,k_t,p). kk-partite planted clique conjecture. If GtG_t is an instance of k-pc(nt,kt,p)k\text{-pc}(n_t,k_t,p), then

lim inf⁡t→∞(PH0[At(G)=1]+PH1[At(G)=0])≥1.\liminf_{t\to\infty}\left(\mathbb{P}_{H_0}[\mathcal{A}_t(G)=1]+\mathbb{P}_{H_1}[\mathcal{A}_t(G)=0]\right)\geq 1.

Thus no such sequence of algorithms distinguishes the planted and null distributions with asymptotic error sum below 11; this is the formal hardness assumption used by the paper's reductions, and the source gives no resolution.

References

Primary source

Matthew Brennan and Guy Bresler, “Average-Case Lower Bounds for Learning Sparse Mixtures, Robust Estimation and Semirandom Adversaries”, arXiv:1908.06130 (2020).

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