Archive for University College Dublin

the HMC algorithm meets the exchange algorithm

Posted in Mountains, pictures, Statistics, Travel, University life with tags , , , , , , , , on July 26, 2017 by xi'an

Julien Stoehr (now in Dublin, soon to join us as a new faculty in Paris-Dauphine!), Alan Benson and Nial Friel (both at UCD) arXived last week a paper entitled Noisy HMC for doubly-intractable distributions. Which considers solutions for adapting Hamiltonian Monte Carlo to target densities that involve a missing constant. In the sense of our workshop last year in Warwick. And in the theme pursued by Nial in the past years. The notion is thus to tackle a density π(θ)∞exp(V(X|θ)/Z(θ) when Z(θ) is intractable. In that case the gradient of log Z(θ) can be estimated as the expectation of the gradient of V(X|θ) [as a standard exponential family identity]. And the ratio of the Z(θ)’s appearing in the Metropolis ratio can be derived by Iain Murray’s exchange algorithm, based on simulations from the sampling distribution attached to the parameter in the denominator.

The resulting algorithm proposed by the authors thus uses N simulations of auxiliary variables at each step þ of the leapfrog part, towards an approximation of the gradient term, plus another N simulations for approximating the ratio of the normalising constants Z(θ)/Z(θ’). While justified from an importance sampling perspective, this approximation is quite poor when θ and θ’ differ. A better solution [as shown in the paper] is to take advantage of all leapfrog steps and of associated auxiliary simulations to build a telescopic product of ratios where the parameter values θ and θ’ are much closer. The main difficulty is in drawing a comparison with the exchange algorithm, since the noisy HMC version is computationally more demanding. (A secondary difficulty is in having an approximate algorithm that no longer leaves the target density stationary.)

Sudokus with minimum number of clues

Posted in Statistics, University life with tags , , on January 9, 2012 by xi'an

Yesterday, I spotted on a Spanish post on the minimal number of clues to solve a Sudoku in a unique way. The original paper was posted on arXiv on January 1, in the Data structure and algorithms category. The authors, Gary McGuire, Bastian Tugemann, and Gilles Civario from University College Dublin, have shown by an exhaustive search that no Sudoku with 16 clues exist (in the sense of having a unique solution). The actual computation actually tooks 7.1 million core hours on the Stokes machine, which is a cluster with 320 nodes, which represents about a year in real time! The Sudoku solver the authors used was Brian Turner’s open source solver. (Here is a review of solvers.) Very impressive…