Bridging the Gap between Constant Step Size Stochastic Gradient Descent and Markov Chains

We consider the minimization of an objective function given access to unbiased estimates of its gradient through stochastic gradient descent (SGD) with constant step-size. While the detailed analysis was only performed for quadratic functions, we provide an explicit asymptotic expansion of the moments of the averaged SGD iterates that outlines the dependence on initial conditions, the effect of noise and the step-size, as well as the lack of convergence in the general (non-quadratic) case. For this analysis, we bring tools from Markov chain theory into the analysis of stochastic gradient. We then show that Richardson-Romberg extrapolation may be used to get closer to the global optimum and we show empirical improvements of the new extrapolation scheme

Preliminary Results on the Empirical Applicability of the Tsallis Distribution in Elastic Hadron Scattering

We show that the proton-proton elastic differential cross section data at dip position and beyond can be quite well described by a parametrization based on the Tsallis distribution, with only five free fit parameters. Extrapolation of the results obtained at 7 TeV to large momentum transfer, suggests that hadrons may not behave as a black-disk at the asymptotic energy region.Comment: 3 pages, 1 figure, version matching proceedings style, XII Hadron Physics, 2012, AIP Proc. Con