How Well Do Local Algorithms Solve Semidefinite Programs?

How Well Do Local Algorithms Solve Semidefinite Programs?

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Andrea Montanari, Stanford University
Fine Hall 214

Several probabilistic models from high-dimensional statistics and machine learning reveal an intriguing -and yet poorly understood- dichotomy. Either simple local algorithms succeed in estimating the object of interest, or even sophisticated semi-definite programming (SDP) relaxations fail.  In order to explore this phenomenon, we study a classical SDP relaxation of the minimum graph bisection problem, when applied to Erdos-Renyi random graphs with bounded average degree d>1, and obtain several types of results. First, we use a dual witness construction (using the so-called non-backtracking matrix of the graph) to upper bound the SDP value. Second, we prove that a simple local algorithm approximately solves the SDP to within a factor 2d^2/(2d^2+d−1) of the upper bound. In particular, the local algorithm is at most 8/9 suboptimal, and 1+O(1/d) suboptimal for large degree.  We then analyze a more sophisticated local algorithm, which aggregates information according to the harmonic measure on the limiting Galton-Watson (GW) tree. The resulting lower bound is expressed in terms of the conductance of the GW tree and matches surprisingly well the empirically determined SDP values on large-scale Erdos-Renyi graphs.  We finally consider the planted partition model. In this case, purely local algorithms are known to fail, but they do succeed if a small amount of side information is available. Our results imply quantitative bounds on the threshold for partial recovery using SDP in this model. [Based on joint work with Zhou Fan]