# News for October 2020

Sorry for the delay in writing this monthly digest: we hope you didn’t spend the week frantically refreshing your browsers! We found four papers this month: let’s dive in.

A Structural Theorem for Local Algorithms with Applications to Coding, Testing, and Privacy, by Marcel Dall’Agnol, Tom Gur, and Oded Lachish (ECCC). This paper introduces the notion of “robust (local) algorithm,” an abstraction which encompasses may types of algorithms: e.g., property testing algorithms, locally decodable codes, etc. The main result of this work is that any (possibly adaptive) $$q$$-query robust local algorithm can be transformed into a non-adaptive, sample-based one making $$n^{1-1/(q^2\log q)}$$ queries (where $$n$$ is the input size). Here, “sample-based” means that the algorithm doesn’t get to make arbitrary queries, but just gets to observe randomly chosen coordinates of the input. As application of this result, the authors derive new upper and lower bounds for several of the types of local algorithms mentioned above, resolving open questions from previous works.

Testing Tail Weight of a Distribution Via Hazard Rate, by Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld (arXiv). The authors consider, from the point of view of distribution testing, the question of deciding whether data is heavy-tailed: as would, for instance, data following a power law. The paper first sets out to formalize the question, and discusses various possible definitional choices before setting on one; after which it provides a test, and analyzes its sample complexity as a function of various parameters (such as smoothness of the unknown distribution). The authors finally back these results with empirical evaluation of their algorithm.

On Testing of Samplers, by Kuldeep S. Meel, Yash Pote, Sourav Chakraborty (arXiv). Suppose you are given a (known) distribution $$p$$ over some domain $$\Omega$$, and want to sample from it conditioned on some predicate $$\varphi$$. Now someone comes to you with an algorithm which does exactly that, efficiently, and cheaply: great! But can you easily check that you’re not getting fooled, and that this sampler actually does what it claims? This paper provides this: an algorithm which accepts if the sampled distribution is $$\varepsilon$$-close to what it should (roughly, in a multiplicative, KL divergence sense), and rejects if it’s $$\varepsilon’$$-far (in total variation distance). The number of samples required is polynomial in $$\varepsilon’-\varepsilon$$, and depends on some characteristic of $$p$$ and $$\varphi$$, the “tilt” (ratio between max and min probability of the conditional distribution).

Finally, an omission from late September:
Sample optimal Quantum identity testing via Pauli Measurements, by Nengkun Yu (arXiv). The abstract is concise and clear enough to speak for itself: “In this paper, we show that $$\Theta(\textrm{poly}(n)\cdot\frac{4^n}{\varepsilon^2})$$ is the sample complexity of testing whether two $$n$$-qubit quantum states $$\rho$$ and $$\sigma$$ are identical or $$\varepsilon$$-far in trace distance using two-outcome Pauli measurements.”

Please let us know if you spotted a paper we missed!

# Welcome Akash Kumar!

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.

# News for January 2017

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.

# Welcome Clément and Gautam!

Dear readers, please welcome the new additions to our moderator group, Clément Canonne and Gautam Kamath! It’s great that more property testing researchers are helping take PTReview further. PTReview is becoming really popular these days! (Or so we hope…)

# News for December 2015

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.