This month’s post will liberally link to previous posts, because it turns out we have written enough in the past to motivate this month’s work. Our news this March has more monotonicity testing, non-deterministic testing, and image testing. Let me test your patience no more…

**On the Complexity of Nondeterministically Testable Hypergraph Parameters**

by Marek Karpinski and Ronald Markó (arXiv). The notion of nondeterministic graph property testing was introduced by Lovász and Vestergombi and further developed by Gishboliner and Shapira, and the authors. Our August 2014 news post explains these results, rather (ahem) beautifully, so check that out. This result generalizes nondeterministic property testing to hypergraphs, and proves the equivalence to deterministic (standard) property testing. Unlike their previous result, this does not give an explicit function for the complexity of a deterministic tester for a specified nondeterministically testable property. (We only know that it is “some” constant.) This is analogous to the original work of Lovász and Vestergombi, which was non-constructive. These non-constructive results use limit theorems for dense (hyper)graphs. This certainly leaves the open question of getting explicit complexity bounds.

**Quantum Algorithm for Monotonicity Testing on the Hypercube**by Aleksandrs Belovs and (PTReview’s very own) Eric Blais (arXiv). Much has been said about Boolean monotonicity testing in our reports, and you can refresh everything by checking out the open problem for Feb 2015. This result gives a \(O(n^{1/4}\epsilon^{-1/2})\) in the quantum testing model (check Ashley Montanaro’s survey on PTReview). Standard quantum amplification of the classic edge tester and the recent Khot-Minzer-Safra monotonicity tester yield \(O(\sqrt{n/\epsilon})\) and \((n^{1/4}/\epsilon)\) testers respectively. The point of this result is to get the best dependence on both \(n\) and \(\epsilon\). For \(\epsilon = 1/\sqrt{n}\), the new quantum algorithm gives a cubic speedup over existing classical testers. From a quantum computing perspective, there are few problems with polynomial super-quadratic speedup, and Boolean monotonicity testing now enters that list.

**Constant-Time Testing and Learning of Image Properties**by Piotr Berman, Meiram Murzabulatov, and Sofya Raskhodnikova (arXiv). The notion of testing image properties was first introduced by Raskhodnikova and further developed by Tsur and Ron. There have been some really interesting results on applying these methods in practice for computer vision. This paper studies different query models for the property tester. Typically, the input is a grid of (black and white) pixels, and we wish to determine if the black region is connected, convex, etc. Previous testers were allowed to query any pixel of choice and could be adaptive. This paper focuses on the restricted sample based models, where the tester only sees uniform random pixels, and variants thereof. Interestingly, this limited power suffices to test and even tolerant test the properties of being a half-plane, connectedness, and convexity. There are numerous results for the different models and tolerant vs standard testers. Having richer models would likely have more impact in practice, where query models may not be feasible. Maybe this result will lead to more work in computer vision!