Smoothed Analysis of Dynamic Networks

We generalize the technique of smoothed analysis to distributed algorithms in dynamic network models. Whereas standard smoothed analysis studies the impact of small random perturbations of input values on algorithm performance metrics, dynamic graph smoothed analysis studies the impact of random perturbations of the underlying changing network graph topologies. Similar to the original application of smoothed analysis, our goal is to study whether known strong lower bounds in dynamic network models are robust or fragile: do they withstand small (random) perturbations, or do such deviations push the graphs far enough from a precise pathological instance to enable much better performance? Fragile lower bounds are likely not relevant for real-world deployment, while robust lower bounds represent a true difficulty caused by dynamic behavior. We apply this technique to three standard dynamic network problems with known strong worst-case lower bounds: random walks, flooding, and aggregation. We prove that these bounds provide a spectrum of robustness when subjected to smoothing---some are extremely fragile (random walks), some are moderately fragile / robust (flooding), and some are extremely robust (aggregation).Comment: 20 page

Soft supersymmetry breaking in the nonlinear sigma model

In this work we discuss the dynamical generation of mass in a deformed ${\cal N}=1$ supersymmetric nonlinear sigma model in a two-dimensional ($D=1+1$) space-time. We introduce the deformation by imposing a constraint that softly breaks supersymmetry. Through the tadpole method, we compute the effective potential at leading order in $1/N$ expansion showing that the model exhibit a dynamical generation of mass to the matter fields. Supersymmetry is recovered in the limit of the deformation parameter going to zero.Comment: 9 pages, 2 figures. Revised version. arXiv admin note: text overlap with arXiv:1308.471