An O(n3 [square root of] log n) algorithm for the optimal stable marriage problem

We give an O(n^3 √logn) time algorithm for the optimal stable marriage problem. This algorithm finds a stable marriage that minimizes an objective function defined over all stable marriages in a given problem instance.Irving, Leather, and Gusfield have previously provided a solution to this problem that runs in O(n^4) time [ILG87]. In addition, Feder has claimed that an O(n^3 log n) time algorithm exists [F89]. Our result is an asymptotic improvement over both cases.As part of our solution, we solve a special blue-red matching problem, and illustrate a technique for simulating Hopcroft and Karp's maximum-matching algorithm [HK73] on the transitive closure of a graph

Lower bounds for the stable marriage problem and its variants

In an instance of the stable marriage problem of size n, n men and n women each ranks members of the opposite sex in order of preference. A stable marriage is a complete matching M = {(m_1, w_i_1), (m_2, w_i_2), ..., (m_n, w_i_n)} such that no unmatched man and woman prefer each other to their partners in M.A pair (m_i, w_j) is stable if it is contained in some stable marriage. In this paper, we prove that determining if an arbitrary pair is stable requires Ω(n^2) time in the worst case. We show, by an adversary argument, that there exists instances of the stable marriage problem such that it is possible to find at least one pair that exhibits the Ω(n^2) lower bound.As corollaries of our results, the lower bound of Ω(n^2) is established for several stable marriage related problems. Knuth, in his treatise on stable marriage, asks if there is an algorithm that finds a stable marriage in less than Θ(n^2) time. Our results show that such an algorithm does not exist

Bayesian Semi-supervised Learning with Graph Gaussian Processes

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural networks in active learning experiments where labels are scarce. Furthermore, the model does not require a validation data set for early stopping to control over-fitting. Our model can be viewed as an instance of empirical distribution regression weighted locally by network connectivity. We further motivate the intuitive construction of the model with a Bayesian linear model interpretation where the node features are filtered by an operator related to the graph Laplacian. The method can be easily implemented by adapting off-the-shelf scalable variational inference algorithms for Gaussian processes.Comment: To appear in NIPS 2018 Fixed an error in Figure 2. The previous arxiv version contains two identical sub-figure

Production of high stellar-mass primordial black holes in trapped inflation

Trapped inflation has been proposed to provide a successful inflation with a steep potential. We discuss the formation of primordial black holes in the trapped inflationary scenario. We show that primordial black holes are naturally produced during inflation with a steep trapping potential. In particular, we have given a recipe for an inflaton potential with which particle production can induce large non-Gaussian curvature perturbation that leads to the formation of high stellar-mass primordial black holes. These primordial black holes could be dark matter observed by the LIGO detectors through a binary black-hole merger. At the end, we have given an attempt to realize the required inflaton potential in the axion monodromy inflation, and discussed the gravitational waves sourced by the particle production.Comment: 6 pages, 5 figures, match the version accepted by JHE