Let’s welcome our latest editor, Akash Kumar. Akash will be taking the place of Gautam Kamath, who has decided to pass the torch on. Let’s also thank Gautam for all the help with PTReview.
2017 starts off rather slow for property testing. Though, we have an intriguing paper to report – an experimental analysis of a classic sublinear algorithm.
Evaluating a Sublinear-time Algorithm for the Minimum Spanning Tree Weight Problem, by Gabriele Santi and Leonardo De Laurentiis (arXiv). The Chazelle-Rubinfeld-Trevisan Minimum Spanning Tree algorithm is a classic in sublinear algorithms. This algorithms provides a \((1+\varepsilon)\) approximation to the MST in time independent of the number of vertices (although it does depend on the average degree). But how this compare with Prim’s algorithm on real instances, in a real (not theoretical) computer? This intriguing paper does a detailed experimental comparison. Having done experimental graph algorithms myself, I can attest to the various challenges: how to choose a test set of graphs? How to set error parameters? Can data structure optimization on the coding side beat asymptotic improvements? This paper does a series of experiments on synthetic graph generators (such as Erdős-Rényi, Barabási-Albert, Watts-Strogatz models). They do validate the basic CRT algorithm at scale, by showing that it is faster than Prim for graphs with more than a million edges. Their experiments suggest that the sublinear-time algorithm gives little benefits when \(\varepsilon \leq 0.2\). The paper has many experiments for a variety of settings, and the authors do a comprehensive study of the various parameters. I’d definitely recommend to anyone interested in exploring how property testing might influence algorithms in the real world.
Greetings from the exciting Workshop on Sublinear Algorithms at John Hopkins University! As this workshop and the upcoming SODA and ITCS conferences get 2016 to a roaring start, let us take one last look back at property testing news from last year. In December, one work in particular caught my eye:
Non-Local Probes Do Not Help with Graph Problems by Mika Göös, Juho Hirvonen, Reut Levi, Moti Medina, and Jukka Suomela (arXiv). A generalization of property testing that has recently seen some fascinating developments in the past few years is the local computation algorithms (LCA) model, in which the algorithm is asked to answer some local query (such as “what is the color of this vertex in some fixed, legal coloring of the graph?”) in sublinear-time. This paper relates the LCA model to message-passing models and in the process gives a powerful new tool for establishing lower bounds in LCAs.