Georgia Institute of Technology College of Sciences

Beginning with Szemerédi’s regularity lemma, the theory of graph decomposition and graph limits has greatly increased our understanding of large dense graphs and provided a framework for graph approximation. Unfortunately, much of this work does not meaningfully extend to non-dense graphs. We present preliminary work towards our goal of creating tools for approximating graphs of intermediate degree (average degree o(n) and not bounded). We give a new random graph model that produces a graph of desired size and density that approximates the number of small closed walks of a given sparse graph (i.e., small moments of its eigenspectrum). We show how our model can be applied to approximate the hypercube graph. This is joint work with Santosh Vempala.

We study stable marriage where individuals strategically submit private preference information to a publicly known stable marriage algorithm. We prove that no stable marriage algorithm ensures actual stability at every Nash equilibrium when individuals are strategic. More specifically, we show that any rational marriage, stable or otherwise, can be obtained at a Nash equilibrium. Thus the set of Nash equilibria provides no predictive value nor guidance for mechanism design. We propose the following new minimal dishonesty equilibrium refinement, supported by experimental economics results: an individual will not strategically submit preference list L if there exists a more honest L' that yields as preferred an outcome. Then for all marriage algorithms satisfying monotonicity and IIA, every minimally dishonest equilibrium yields a sincerely stable marriage. This result supports the use of algorithms less biased than the (Gale-Shapley) man-optimal, which we prove yields the woman-optimal marriage in every minimally dishonest equilibrium. However, bias cannot be totally eliminated, in the sense that no monotonic IIA stable marriage algorithm is certain to yield the egalitarian-optimal marriage in a minimally dishonest equilibrium – thus answering a 28-year old open question of Gusfield and Irving's in the negative. Finally, we show that these results extend to student placement problems, where women are polygamous and honest, but not to admissions problems, where women are both polygamous and strategic. Based on joint work with Craig Tovey at Georgia Tech.

Optimization problems arising in decentralized multi-agent systems have gained significant attention in the context of cyber-physical, communication, power, and robotic networks combined with privacy preservation, distributed data mining and processing issues. The distributed nature of the problems is inherent due to partial knowledge of the problem data (i.e., a portion of the cost function or a subset of the constraints is known to different entities in the system), which necessitates costly communications among neighboring agents. In this talk, we present a new class of decentralized first-order methods for nonsmooth and stochastic optimization problems which can significantly reduce the number of inter-node communications. Our major contribution is the development of decentralized communication sliding methods, which can skip inter-node communications while agents solve the primal subproblems iteratively through linearizations of their local objective functions.This is a joint work with Guanghui (George) Lan and Yi Zhou.

We present an algorithm that, with high probability, generates a random spanning treefrom an edge-weighted undirected graph in O (n^{5/3}m^{1/3}) time. The tree is sampled from adistribution where the probability of each tree is proportional to the product of its edge weights.This improves upon the previous best algorithm due to Colbourn et al. that runs in matrix multiplication time, O(n^\omega). For the special case of unweighted graphs, this improves upon thebest previously known running time of ˜O(min{n^\omega, mn^{1/2}, m^{4/3}}) for m >> n^{7/4} (Colbourn et al. ’96, Kelner-Madry ’09, Madry et al. ’15).The effective resistance metric is essential to our algorithm, as in the work of Madry et al., butwe eschew determinant-based and random walk-based techniques used by previous algorithms.Instead, our algorithm is based on Gaussian elimination, and the fact that effective resistance ispreserved in the graph resulting from eliminating a subset of vertices (called a Schur complement).As part of our algorithm, we show how to compute -approximate effective resistances for a set Sof vertex pairs via approximate Schur complements in O (m + (n + |S|)/\eps^{ −2}) time, without usingthe Johnson-Lindenstrauss lemma which requires eO(min{(m+|S|) \eps{−2},m+n /eps^{−4} +|S|/eps^{ −2}}) time. We combine this approximation procedure with an error correction procedure for handing edges where our estimate isn’t sufficiently accurate.Joint work with Rasmus Kyng, John Peebles, Anup Rao, and Sushant Sachdeva