Algorithmic Obstructions in the Random Number Partitioning Problem
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We consider the algorithmic problem of finding a near-optimal solution for the number partitioning problem (NPP). The NPP appears in many applications, including the design of randomized controlled trials, multiprocessor scheduling, and cryptography; and is also of theoretical significance. It possesses a so-called statistical-to-computational gap: when its input X has distribution 𝒩(0,I_n), its optimal value is Θ(√(n)2^-n) w.h.p.; whereas the best polynomial-time algorithm achieves an objective value of only 2^-Θ(log^2 n), w.h.p. In this paper, we initiate the study of the nature of this gap. Inspired by insights from statistical physics, we study the landscape of NPP and establish the presence of the Overlap Gap Property (OGP), an intricate geometric property which is known to be a rigorous evidence of an algorithmic hardness for large classes of algorithms. By leveraging the OGP, we establish that (a) any sufficiently stable algorithm, appropriately defined, fails to find a near-optimal solution with energy below 2^-ω(n log^-1/5 n); and (b) a very natural MCMC dynamics fails to find near-optimal solutions. Our simulations suggest that the state of the art algorithm achieving 2^-Θ(log^2 n) is indeed stable, but formally verifying this is left as an open problem. OGP regards the overlap structure of m-tuples of solutions achieving a certain objective value. When m is constant we prove the presence of OGP in the regime 2^-Θ(n), and the absence of it in the regime 2^-o(n). Interestingly, though, by considering overlaps with growing values of m we prove the presence of the OGP up to the level 2^-ω(√(nlog n)). Our proof of the failure of stable algorithms at values 2^-ω(n log^-1/5 n) employs methods from Ramsey Theory from the extremal combinatorics, and is of independent interest.
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