Author Archives: Akash

News for August 2021

This month saw four papers. One on group testing, another on distribution testing, yet another which makes progress on testing problems on decision trees and the last one on graph property testing. Without further ado, let’s dive in.

Group Testing with Non-identical Infection Probabilities by Mustafa Doger, Sennur Ulukus (arXiv) Consider the classic group testing problem. Here the setup is the following. You are given a bunch of individuals from a population \(\mathcal{P}\). You have an infection vector which records the infection status of each individual in the population where the \(i\)-th individual is infected with probability \(p_i\). You want to recover all the infected individuals. You are allowed to group individuals together and you can test the entire group in a single shot. If the group tests negative, you are happy all the tested individuals are off the hook. Otherwise, if the group tests positive, you need more tests for further classification. This paper proposes a greedy way to build pools of individuals you would test. The pools are built adaptively: as in future pools are built using the knowledge of how the preceding tests fared. The key result in the paper upperbounds the number of tests performed in terms of the entropy of the infection vector.

Uniformity Testing in the Shuffle Model: Simpler, Better, Faster by Clément L. Canonne, Hongyi Lyu (arXiv) Differentially private distribution testing as a research area has been gathering momentum steadily over the last few years. If you read our last month’s post, you might recall there are a wide variety of models of DP each corresponding to a different “threat model”. The most stringent among the most explored models is the “local model”, the least stringent being the “central model” and there is an intermediate threat model, the so called “shuffle model“. This paper simplifies the analysis of uniformity testing algorithm under the shuffle model and presents an algorithm with sample complexity \(O(k^{3/4})\) for testing uniformity over a support of size \([k]\).

On Learning and Testing Decision Tree by Nader H. Bshouty, Catherine A. Haddad-Zaknoon (arXiv) In our December 2020 post, we covered a result of Blanc et al., which proves the following: Suppose you are given a boolean function \(f\) and the property \(\mathcal{P}\) of size-\(s\) decision trees. The result of Blanc et al gives you a function \(g \in \mathcal{P}\) with \(dist(f,g) = O(dist (f, \mathcal{P}))\) where \(g \in \) is guaranteed to have decision tree complexity \(s^{O(\log^2 s)}\). This result implies a bi-criteria tester for the following property: is \(f \in \mathcal{P}\) or is \(f\) \(\varepsilon\)-far from having decision tree complexity \(\phi(s) = s^{O(\log^3 s)}\). The current paper improves this result by presenting a property tester with \(\phi(s) = s^{O(\log^2 s)}\).

The complexity of testing all properties of planar graphs, and the role of isomorphism by Sabyasachi Basu, Akash Kumar, C. Seshadhri (arXiv) (Disclaimer: I am one of the authors of this paper). This paper presents a result that I, in my biased opinion, find interesting. So, here is the setup. You are given a bounded degree planar graph. And I cook up some God-forsaken property and ask you to test it. Turns out, no matter how devilishly I cooked up the property, you can test in with \(\exp(O(\varepsilon^{-2}))\) queries. The nice happenstance is that you also have a matching lower bound of \(\exp(\Omega(\varepsilon^{-2}))\) queries! And interestingly, this lower bound is witnessed by the very natural property of testing isomorphism to a fixed graph which means that isomorphism is the hardest property of planar graphs.

News for May 2021

We hope you are all staying safe. With massive vaccination programs across the globe we hope you and your loved ones are getting back to what used to be normal. With that out of the way, let us circle back to Property Testing. This month was less sleepy as compared to the two preceding months and we saw six papers in total (two of them explore problems in quantum property testing). Without further ado, let us take a deeper dive.

GSF-locality is not sufficient for proximity-oblivious testing, by Isolde Adler, Noleen Kohler, Pan Peng (arXiv) The notion of proximity oblivious testers was made explicit in the seminal work of Goldreich and Ron in 2009 [GR09]. A proximity oblivious tester for a graph property is a constant query tester that rejects a graph with probability that monotonically increases with distance to the property. (Edit: Correction) A property is called proximity oblivious testable (or PO testable) if it has a one sided proximity oblivious tester. [GR09] gave a characterization of which properties \(\Pi\) are PO testable in the bounded degree model if and only if it is a “local” property of some kind which satisfies a certain non propagation condition. [GR09] conjectured that all such “local” properties satisfy this non propagation condition. This paper refutes the above conjecture from [GR09].

Coming up next. More action on triangle freeness.

Testing Triangle Freeness in the General Model in Graphs with Arboricity \(O(\sqrt n)\), by Reut Levi (arXiv) PTReview readers are likely to be aware that triangle freeness has been a rich source of problems for developing new sublinear time algorithms. This paper considers the classic problem of testing triangle freeness in general graphs. In the dense case, algorithms with running time depending only on \(\varepsilon\) are known thanks to the work of Alon, Fischer, Krivelevich and Szegedy. In the bounded degree case, Goldreich and Ron gave testers with query complexity \(O(1/\varepsilon)\). This paper explores the problem in general graph case and proves an upper bound of \(O(\Gamma/d_{avg} + \Gamma)\) where \(\Gamma\) is the arboricity of the graph. The author also shows that this upperbound is tight for graphs with arboricity at most \(O(\sqrt n)\). Curiously enough, the algorithm does not take arboricity of the graph as an input and yet \(\Gamma\) (the arboricity) shows up in the upper and lower bounds.

Testing Dynamic Environments: Back to Basics, by Yonatan Nakar and Dana Ron (arXiv) Goldreich and Ron introduced the problem of testing “dynamic environments” in 2014. Here is the setup for this problem. You are given an environment that evolves according to a local rule. Your goal is to query some of the states in the system at some point of time and determine if the system is evolving according to some fixed rule or is far from it. In this paper, the authors consider environments defined by elementary cellular automata which evolve according to threshold rules as one of the first steps towards understanding what makes a dynamic environment tested efficiently. The main result proves the following: if your local rules satisfy some conditions, you can use a meta algorithm with query complexity \(poly(1/\varepsilon)\) which is non adaptive and has one sided error. And all the threshold rules indeed satisfy these conditions which means they can be tested efficiently.

Identity testing under label mismatch, by Clement Canonne and Karl Wimmer (arXiv) This paper considers a classic problem distribution testing with the following twist. Let \(q\) denote a distribution supported on \([n]\). You are given access to samples from another distribution \(p\) where \(p = q \circ \pi\) where \(\pi\) is some unknown permutation. Thus, I relabel the data and I give you access to samples from the relabeled dataset. Under this promise, note that identity testing becomes a trivial problem if \(q\) is known to be uniform over \([n]\). The authors develop algorithms for testing and tolerant testing of distributions under this additional promise of \(p\) being a permutation of some known distribution \(q\). The main result shows as exponential gap between the sample complexity of testing and tolerant testing under this promise. In particular, identity testing under the promise of permutation has sample complexity \(\Theta(\log^2 n)\) whereas tolerant identity testing under this promise has sample complexity \(\Theta(n^{1-o(1)})\).

Testing symmetry on quantum computers, by Margarite L. LaBorde and Mark M. Wilde (arXiv) This paper develops algorithms which test symmetries of a quantum states and changes generated by quantum circuits. These tests additionally also quantify how symmetric these states (or channels) are. For testing what are called “Bose states” the paper presents efficient algorithms. The tests for other kinds of symmetry presented in the paper rely on some aid from a quantum prover.

Quantum proofs of proximity, by Marcel Dall’Agnol, Tom Gur, Subhayan Roy Moulik, Justin Thaler (ECCC) The sublinear time (quantum) computation model has been gathering momentum steadily over the past several years. This paper seeks to understand the power of \({\sf QMA}\) proofs of proximity for property testing (recall \({\sf QMA}\) is the quantum analogue of \({\sf NP}\)). On the algorithmic front, the paper develops sufficient conditions for properties to admit efficient \({\sf QMA}\) proofs of proximity. On the complexity front, the paper demonstrates a property which admits an efficient \({\sf QMA}\) proof but does not admit a \({\sf MA}\) or an interactive proof of proximity.

News for February 2021

We got quite some action last month. We saw five papers. A lot of action in graph world and some action in quantum property testing which we hope you will find appetizing. Also included is a result on sampling uniformly random graphlets.

Testing Hamiltonicity (and other problems) in Minor-Free Graphs, by Reut Levi and Nadav Shoshan (arXiv). Graph Property Testing has been explored pretty well for dense graphs (and reasonably well for bounded degree graphs). However, testing properties in the general case still remains an elusive goal. This paper makes contributions in this direction and as a first result it gives an algorithm for testing Hamiltonicity in minor free graphs (with two sided error) with running time \(poly(1/\varepsilon)\). Let me begin by pointing out that Hamiltonicity is an irksome property to test in the following senses.

  • It is neither monotone nor additive. So the partition oracle based algorithms do not immediately imply a tester (with running time depending only on \(\varepsilon\) for Hamiltonicity. This annoyance bugs you even in the bounded degree case.
  • Czumaj and Sohler characterized what graph properties are testable with one-sided error in general planar graphs. In particular, they show a property of general planar graphs is testable iff this property can be reduced to testing for a finite family of finite forbidden subgraphs. Again, Hamiltonicity does not budge to this result.
  • There are (concurrent) results by Goldreich and Adler-Kohler which show that with one-sided error, Hamiltonicity cannot be tested with \(o(n)\) queries.

The paper shows that distance to Hamiltonicity can be exactly captured in terms of a certain combinatorial parameter. Thereafter, the paper tries to estimate this parameter after cleaning up the graph a little. This allows them to estimate the distance to Hamiltonicity and thus also implies a tolerant tester (restricted to mino-free graphs).

Testing properties of signed graphs, by Florian Adriaens, Simon Apers (arXiv). Suppose I give you a graph \(G=(V,E)\) where all edges come with a label: which is either “positive” or “negative”. Such signed graphs are used to model various scientific phenomena. Eg, you can use these to model interactions between individuals in social networks into two categories like friendly or antagonistic.

This paper considers property testing problems on signed graphs. The notion of farness from the property extends naturally to these graphs (both in the dense graph model and the bounded degree model). The paper contains explores three problems in both of these models: signed triangle freeness, balance and clusterability. Below I will zoom into the tester for clusterability in the bounded degree setting developed In the paper. A signed graph is considered clusterable if you can partition the vertex set into some number of components such that the edges within any component are all positive and the edges running across components are all negative.

The paper exploits a forbidden subgraph characterization of clusterability which shows that any cycle with exactly one negative edge is a certificate of non-clusterability of \(G\). The tester runs multiple random walks from a handful of start vertices to search for these “bad cycles” by building up on ideas in the seminal work of Goldreich and Ron for testing bipariteness. The authors put all of these ideas together and give a \(\widetilde{O}(\sqrt n)\) time one-sided tester for clusterability in signed graphs.

Local Access to Random Walks, by Amartya Shankha Biswas, Edward Pyne, Ronitt Rubinfeld (arXiv). Suppose I give you a gigantic graph (with bounded degree) which does not fit in your main memory and I want you to solve some computational problem which requires you to solve longish random walks of length \(t\). And lots of them. It would be convenient to not spend \(\Omega(t)\) units of time performing every single walk. Perhaps it would work just as well for you to have an oracle which provides query access to a \(Position(G,s,t)\) oracle which returns the position of a walk from \(s\) at time \(t\) of your choice. Of course, you would want the sequence of vertices returned to behave consistently with some actual random walk sampled from the distribution of random walks starting at \(s\). Question is: Can I build you this primitive? This paper answers this question in affirmative  and shows that for graphs with spectral gap \(\Delta\), this can be achieved with running time \(\widetilde{O}(\sqrt n/\Delta)\) per query. And you get the guarantee that the joint distribution of the vertices you return at queried times is \(1/poly(n)\) close to the uniform distribution over such walks in \(\ell_1\).  Thus, for a random \(d\)-regular graph, you get running times of the order \(\widetilde{O}(\sqrt n)\) per query. The authors also show tightness of this result by showing to get subconstant error in \(\ell_1\), you necessarily need \(\Omega(\sqrt n/\log n)\) queries in expectation.

Efficient and near-optimal algorithms for sampling connected subgraphs, by Marco Bressan (arXiv). As the title suggests, this paper considers efficient algorithms for sampling a uniformly random \(k\)-graphlet from a given graph \(G\) (for \(k \geq 3\)). Recall, a \(k\)-graphlet refers to a collection of \(k\)-vertices which induce a connected graph in \(G\). The algorithm considered in the paper is pretty simple. You just define a Markov Chain \(\mathcal{G}_k\) with all \(k\)-graphlets as its state space. Two states in \(\mathcal{G}_k\) are adjacent iff their intersection is a \((k-1)\)-graphlet. To obtain a uniformly random sample, a classical idea is to just run this Markov Chain and obtain an \(\varepsilon\)-uniform sample. However, the gap between upper and lower bounds on the mixing time of this walk is of the order \(\rho^{k-1}\) where \(\rho = \Delta/\delta\) (that is the ratio of maximum and minimum degrees to the power \(k-1\)). The paper closes this gap up to logarithmic factors and shows that the mixing time of the walk is at most \(t_{mix}(G) \rho^{k-1} \log(n/\varepsilon)\). It also proves an almost matching lower bound. Further, the paper also presents an algorithm with event better running time to return an almost uniform \(k\)-graphlet. This exploits a previous observation: sampling a uniformly random \(k\)-graphlet is equivalent to sampling a uniformly random edge in \(\mathcal{G}_{k-1}\). The paper then proves a lemma which upperbounds the relaxation time of walks in \(\mathcal{G}_k\) to walks in \(\mathcal{G}_{k-1}\). And then you upperbound the mixing time in terms of the relaxation time to get an improved expected running time of the order \(O(t_{mix}(G) \cdot \rho^{k-2} \cdot \log(n/\varepsilon)\).

Toward Instance-Optimal State Certification With Incoherent Measurements, by Sitan Chen, Jerry Li, Ryan O’Donnell (arXiv). The problem of quantum state certification has gathered interest over the last few years. Here is the setup: you are given a quantum state \(\sigma \in \mathbb{C}^{d \times d}\) and you are also given \(N\) copies of an unknown state \(\rho\). You want to distinguish between the following two cases: Does \(\rho = \sigma\) or is \(\sigma\) at least \(\varepsilon\)-far from \(\rho\) in trace norm? Badescu et al showed in a recent work that if entangled measurements are allowed, you can do this with a mere \(O(d/\varepsilon^2)\) copies of \(\rho\). But using entangled states comes with its own share of problems. On the other hand if you disallow entanglement, as Bubeck et al show, you need \(\Omega(d^{3/2}/\varepsilon^2)\) measurements. This paper asks: for which states \(\sigma\) can you improve upon this bound. The work takes inspirations from a la “instance optimal” bounds for identity testing. Authors show a fairly general result which (yet again) confirms that the quantum world is indeed weird. In particular, the main result of the paper implies that the copy complexity of (the quantum analog of) identity testing in the quantum world (with non-adaptive queries) grows as \(\Theta(d^{1/2}/\varepsilon^2)\). That is, the number of quantum measurements you need increases with \(d\) (which is the stark opposite of the behavior you get in the classical world).

News for November 2020

Highlights in property testing this month, include developments across the board. We have a nice potpourri of results which will catch your fancy. From a result which shows the tightness of quadratic gap between query complexities of adaptive vs non-adaptive testers for dense graph properties, to isoperimetric Talagrand-like inequalities for real valued functions over the hypercube, to results which consider face off between quantum computing and distribution testing and more. Looks like the festive season came early for the PTReview readers.

Non-adaptive vs Adaptive Queries in the Dense Graph Testing Model (by Oded Goldreich and Avi Wigderson) (ECCC) It has been long established in the dense graph property testing literature that the query complexity of adaptive testers and non-adaptive testers are only a quadratic factor apart. This work asks: is this gap necessarily tight and answers in the affirmative. The main conceptual tool used in the paper is the notion of robustly self-ordered graphs and local self-ordering procedures for these graphs (which was developed by the same authors and was covered in our September Post). These results use these tools to port lower bounds on property testing problems for bit strings to graph properties.

Direct Sum and Partitionability Testing over General Groups (by Andrej Bogdanov and Gautam Prakriya) (ECCC) In their celebrated work, Blum, Luby and Rubinfeld gave a four query test to determine whether a function \(f \colon \mathbb{F}_2^n \to \mathbb{F}_2\) is affine. This paper considers a generalization of the notion of affinity to functions \(f \colon \{0,1\}^n \to G\) where \(G\) is an Abelian group. The generalization sough asks: does \(f\) have the form \(f = \sum x_ig_i + g_0\) for group elements \(g_0, g_1, \cdots, g_n \in G\). In this setting, the BLR analysis does not apply as the domain and range are note equipped with the same group structure. This work presents a four query test for the above problem. In fact, the test is more general and can be used to decide whether a function \(f \colon D_1 \times D_2 \ldots \times D_n \to G\) from a product domain \(D_1 \times D_2 \ldots \times D_n\) to an Abelian group $G$ is a direct sum if it has the form \(f(x_1, x_2, \cdots, x_n) = \sum f_i(x_i)\) which resolves a conjecture of Dinur and Golubev. The work also presents results for testing whether a function \(f\) defined from a product domain to an Abelian group \(G\) is a direct product.

Isoperimetric Inequalities for Real-Valued Functions with Applications to Monotonicity Testing (by Hadley Black, Iden Kalemaj and Sofya Raskhodnikova) (ArXiv) This paper generalizes the celebrated isoperimetric inequalities of boolean functions over the hypercube to the setting of real valued functions. This generalized isoperimetry is then put to work to obtain a monotonicity tester for \(f \colon \{0,1\}^n \to \mathbb{R}\). The tester makes \(\min(\tilde{O}(r \sqrt n), O(n))\) non-adaptive queries (where \(r\) is the size of image of \(f\)) and has one-sided error. The authors generalize the KMS inequality by a Boolean decomposition theorem which allows them to represent any real valued function over the cube as a collection of Boolean functions over the cube which capture \(f\)’s distance to montonicity as well the structure of violations to monotonicity in \(f\).

Erasure-Resilient Sublinear-Time Graph Algorithms (by Amit Levi, Ramesh Krishnan S. Pallavoor, Sofya Raskhodnikova and Nithin Varma) (ArXiv) In a previous work, a subset of the authors explored how to equip property testers with the erasure-resilience for function properties. With graph properties, a different picture emerges. In the dense graph model, where you have query access to the adjacency matrix, the situation is still fine: adjacency matrices are functional representations of graphs: therefore, if you have a black belt in making property testers erasure resilient for function properties, you would be able to test properties of dense graphs too. However, if I give you query access to the graph through an adjacency list, the picture changes. These are non-functional representations of graphs and therefore need new conceptual tools. This paper begins the study of erasure resilience in the adjacency list model. It focuses on two computational tasks: testing connectivity and estimating average degree. Let me showcase the results in the paper on testing connectivity. It is shown that you encounter a threshold phenomena in testing connectivity. As long as the fraction of erasures is small, you get testers which run in time independent of the size of the graph. But when the fraction of erasures exceeds a certain cutoff, it is shown that the tester needs a number of queries linear in the size of the adjacency list.

Expander Random Walks: A Fourier Analytic Approach (by Gil Cohen, Noam Peri and Amnon Ta-Shma) (ECCC) While this is a not exactly a property testing paper, how can you not report a paper which proves a significant strengthening of the classic CLT for Markov Chains (alright, for random walks on expanders) with respect to the TV distance? Let us now unpack the above a little. So, consider the following setup: Suppose you have a \(d\)-regular expander \(G\). Now, imagine running the following process \(\textsf{(P)}\):

1) Label half the vertices in \(G\) as \(+1\) and the other half as \(-1\) arbitrarily.
2) Take a \(t-1\) step random walk which involves \(t\) vertices: say \(v_1, v_2, \ldots v_t\).
3) Finally, return the boolean label of all of the visited vertices. This gives a bit-vector \(x \in \{-1,1\}^t\).

Now, let us ask: Which class of functions get fooled by this process?

The main result of the paper shows that this process fools

1) Symmetric functions
2) The class \(AC^0\)
3) Functions \(f\) which are computable by low width read once branching programs.

In more detail, let \(f\) be a symmetric function and let \(G\) be a \(\lambda\)-spectral expander. For the process \(\textsf{(P)}\) defined above, it follows by the spectral expansion of \(G\), that \(discrepancy(f) = |E_{x \sim \textsf{(P)}} [f(x)] – E_{x \sim U_t} [f(x)]|\) is small.

Now note that the total variation distance is precisely equal to the maximum discrepancy you get over all symmetric functions \(f\). This leads to the connection that allows the authors to upgrade CLT for expander random walks as they are able to show a CLT for Markov Chains with respect to the TV distance (as opposed to the CLT with respect to the Kolmogorov Distance which is what was known from the work of Kipnis and Vadhan since 1986).

New Sublinear Algorithms and Lower Bounds for LIS Estimation (by Ilan Newman and Nithin Varma) (ArXiv) As the title suggests, this paper considers the task of estimating the length of the longest increasing sequence in an array. In particular, it gives a non-adaptive algorithm which estimates the LIS length within an additive error of \(\pm \varepsilon n\) in \(\tilde{O}\left(\frac{r}{\varepsilon^3}\right)\) queries (where \(r\) is the number of distinct elements in \(A\)). On the lower bound side, the paper presents a lower bound of \((\log n)^{\Omega(1/\varepsilon)}\) non-adaptive queries. The lower bound construction uses ideas from lower bound of Ben-Eliezer, Canonne, Letzter and Waingarten on testing monotone pattern freeness.

Stability and testability: equations in permutations (by Oren Becker, Alexander Lubotzky, AND Jonathan Mosheiff) (ArXiv) Consider the following question: Suppose I ask if two permuations \(A,B \in Sym(n)\). Suppose I want to decide whether these permutations commute or whether they are far from permutations which commute. Can this question be answered in time independent of \(n\)? The authors answer this question in the affirmative. They show a simple Sample and Substitute procedure which essentially does the following: take samples \(x_1, x_2, cdots x_k \sim [n]\). And accept iff \(ABx_i = BAx_i\) for each \(i\). The authors call this particular set of equations/constraints (checking whether \(AB = BA\)) stable: as it admits a Sample and Substitute style tester with query complexity independent of \([n]\). This allows the authors to consider the group theoretic notion of stability as a property testing concept and allows them to examine the notion of stability from a computational standpoint.

StoqMA meets distribution testing (by Yupan Liu) (ArXiv) This paper shows a containment result. A certain complexity class \(\textsf{eStoqMA}\) is shown to be a subset of \(\textsf{StoqMA}\). Let us informally define what these classes are. A language \(L \in \textsf{StoqMA}\) if there exists a verifier (which is a reversible quantum circuit) such that on input a string \(x\) such that for every \(x \in L\) there exists a witness quantum state which makes the verifier accept with probability at least \(2/3\). And in case \(x \not\in L\), for every quantum state the verifier rejects \(x\) with probability at least \(2/3\). The paper characterizes this class from a distribution testing point of view. And this connection allows the author to conclude that \(\textsf{eStoqMA} = \textsf{MA}\). Here, \(\textsf{eStoqMA}\) is a sub-class of \(\textsf{StoqMA}\) where all YES instances have an easy witness.

(Edit: Added later) We missed the following two papers. Thanks to our readers for pointing it out.

Near-Optimal Learning of Tree-Structured Distributions by Chow-Liu (by Arnab Bhattacharya, Sutanu Gayen, Eric Price and N.V. Vinodchandran) (ArXiv) Graphical models are a convenient way to describe high dimensional data in terms of its dependence structure. An important question in this area concerns learning/inferring the underlying graphical model from iid samples. This paper considers the problem when the underlying graph is in fact a tree. Thus, the challenge is to learn a tree structured distribution. The main result is that given sample access to a tree structured distribution, you can recover an \(\varepsilon\) approximate tree with good probability in \(O(n/\varepsilon)\).

Relaxed Locally Correctable Codes with Improved Parameters (by Vahid R. Asadi and Igor Shinkar) (ECCC) In their work on Robust PCPs of Proximity, Ben-Sasson et al. asked whether it is possible to obtain a \(q\)-query (Relaxed) Locally Decodable Code whose block length is strictly smaller than the best known bounds on block lengths for Locally Decodable Codes. Recall with relaxed LDCs you are allowed to abort outputting the \(i^{th}\) bit of the message should you detect that the received word is not a valid codeword. This paper makes progress on this problem and shows that you can actually construct an \(O(q)\)-query relaxed LDCs which encode a message of length \(k\) using \(O(k^{1 + 1/q})\) queries. This matches some of the known lower bounds for constant query LDCs which thus makes progress towards understanding the gap between LDCs and relaxed LDCs in the regime \(q = O(1)\).

News for August 2020

Last month saw action in property testing across the board: graphs, distributions and functions were all objects of study in papers that came out last month. Also included is a separation result between quantum and classical query complexities resolving a conjecture of Aaronson and Ambainis. Here is a brief report.

Testing asymmetry in bounded degree graphs, by Oded Goldreich (ECCC). This paper studies a natural graph property hitherto not considered in the property testing literature. Namely, the question of testing whether a graph is asymmetric or whether it is far from being asymmetric. A graph is said to be asymmetric if its automorphism group is trivial (that is, it only contains the identity permutation). One of the results in the paper says that this problem is easy in the dense graph model – which is a side result of the paper. This is because all dense graphs are \(O(\log n/n)\)-close to being asymmetric. To see this, the paper points out that a simple randomized process which takes \(G\) as input and returns an asymmetric graph by changing very few edges. This process asks you to do the following: Take a set \(S \subseteq V\) with \(|S| = O(\log n)\) nodes and replace the subgraph they induce with a random graph. Moreover,  randomize all the edges between \(S\) and \(V \setminus S\). What you can show is that in this modified graph, any automorphism (whp) will map \(S\) to itself. And all the remaining vertices behave (whp) in a unique manner which is peculiar to it. In particular, this means that any automorphism better not map a vertex \(v\) in \(V \setminus S\) to any other vertex in \(V \setminus S\). And this finishes the argument. The main result explores the bounded degree model. By a simple argument, you can show that testing asymmetry is easy if all the connected components have size \(s(n) \leq o\left(\frac{\log n}{\log {\log n}}\right)\). Thus, the challenging case is when you have some connected components of a larger size. In this case, what Goldreich shows is the following: If all the components have size at most \(s(n)\) where \(s(n) \geq \Omega\left(\frac{\log n}{\log {\log n}}\right)\), then you can test asymmetry (with one-sided-error) in \(O(n^{1/2} \cdot s(n)/\epsilon)\) queries.  Moreover, the paper also shows a two-sided-lower bound of \(\Omega\left(n/s(n)\right)^{1/2}\) queries which holds as long as \(\epsilon \leq O(1/s(n)\). This leaves open the bounded degree question of determining the query complexity of testing asymmetry in the general case as the paper also points out.

On testability of first order properties in Bounded degree graphs, by Isolde Adler, Noleen Köhler and Pan Peng (arXiv). One of the well understood motivations for property testing begins in the following manner. Decision problems are typically hard to solve if they involve a universal quantifier — either \(\exists\) or \(\forall\). One way around this hardness is to do the following ritual: relax the problem by dropping the universal quantifier, define a notion of distance between objects in your universe and ask a promise problem instead. Indeed, if you take your favorite property testing problem, you will note that it precisely fits in the template above. How about making this business more rigorous in bounded degree graph property model? This is precisely the content of this work which considers the face off between (First Order) logic and property testing in the bounded degree model. The authors show some interesting results. They begin by showing that in the bounded degree model, you can show that all first order graph properties which involve a single quantifier \(Q \in \{\forall, \exists\}\) are testable with constant query complexity.
If you find this baffling, it would be good to remind yourself that not all graph properties can be expressed in the language of first order logic with a single quantifier! So, you can rest easy. The graph properties you know are not constant time testable are most assuredly not expressible with a single quantifier in First Order Logic. However, this work shows more. It turns out, that “any FO property that is defined by a formula with quantifier prefix \(\exists^* \forall^*\) is testable”. Moreover, there do exist FO graph properties defined by the quantifier prefix \(\forall^* \exists^*\) which are not testable. Thus, this work achieves results in the bounded degree model which are kind of analogous to the results in the dense graph model by Alon et al [1]. On a final note, I find the following one sentence summary of the techniques used to prove the lower bound rather intriguing: “[the paper] obtains the lower bound by a first-order formula that defines a class of bounded-degree expanders, based on zig-zag products of graphs.”

On graphs of bounded degree that are far from being Hamiltonian, by Isolde Adler and Noleen Köhler (arXiv). This paper explores the question of testing Hamiltonicity in the bounded degree model. The main result of the paper is that Hamiltonicity is not testable with one-sided error with \(o(n)\) queries. PTReview readers might recall from our July Post a concurrent paper by Goldriech [2] which achieves the same lower bound on query complexity in the two-sided error model (the authors call attention to [2] as well). One of the interesting feature of this result is that the lower bounds are obtained by an explicit deterministic reduction as opposed to the usual randomized reduction. Like the authors point out, this offers more insights into structural complexity of instances that are far from being Hamiltonian. We point out that this also differs from how the lower bound is derived in [2] — which is via local hardness reductions to a promise problem of 3 CNF satisfiability.

An optimal tester for \(k\)-linear, by Nader Bshouty (ECCC). This paper explores two related questions. We call a function \(f \colon \{0,1\}^n \to \{0,1\}\) \(k\)-linear if it equals the \(\sum_{i \in S} x_i\) for some \(S \subseteq [n]\) of size exactly \(k\). A boolean function is said to be \(k\)-linear* if it is \(j\) linear for a fixed \(j\) where \(j \in \{0,1,2, \cdots, k\}\). The paper proves the following theorems.

  1. There exists a non-adaptive one-sided distribution free tester for \(k\)-linear* with query complexity being \(O\left(k \log k + \frac{1}{\varepsilon}\right)\). This matches the two-sided lower bound (where the underlying distribution is uniform) by Blais et al [3].
  2. Using a reduction from \(k\)-linear* to \(k\)-linear, the paper shows one can obtain a non-adpative two-sided distribution free tester for \(k\)-linear with same query complexity as the above result. The lower bound from Blais et al applies here also (in fact, they prove a lower bound on \(k\)-linearity).
  3. Next up, the paper has a couple of lower bound results to accompany this. One of these results reveals the price you pay for being one-sided and exact (that is, you insist on the function being exactly \(k\)-linear). Turns out, now you have a non-adaptive one-sided uniform distribution lower bound of \(\widetilde{\Omega}(k) \log n + \Omega(1/\varepsilon)\). If you allow adaptivity instead, the paper shows a lower bound of \(\widetilde{\Omega}(\sqrt k)\log n + \Omega(1/\varepsilon)\).

Amortized Edge Sampling, by Talya Eden, Saleet Mossel and Ronitt Rubinfeld (arXiv). Consider the following setup. You are given query access to adjacency list of a graph \(G\) with \(n\) vertices and \(m\) edges. You can make degree queries and neighbor queries. Suppose I ask you to sample a single edge from this graph from a distribution that is pointwise \(\varepsilon\) close to the uniform distribution. Eden and Rosenbaum already showed how you can achieve this with a budget of \(\widetilde{\Theta}(n/\sqrt m)\) queries. Starting from this jump off point, the authors ask whether you can circumvent this lower bound if you want to return multiple samples from a distribution which is again pointwise close to uniform. The paper answers this question in the affirmative and shows that if you know the number of samples, \(q\), in advance you can get away with an amortized bound of \(O*(\sqrt q n/\sqrt m)\) on the total number of queries needed.

On the High Accuracy Limitation of Adaptive Property Estimation, by Yanjun Han (arXiv). Take a discrete distribution \(\mathcal{P}\) with support size \(k\) and consider the task of estimating some symmetric property of \(\mathcal{P}\) to a small \(\pm \varepsilon\) additive error. Here, a symmetric property refers to a “nice” functional defined over the probability simplex, i.e., it refers to functions \(F \colon \Delta_k \to \mathbb{R}\) where \(F(p) = \sum_{i=1}^{k} f(p_i)\) where \(f \colon (0,1) \to \mathbb{R}\). A naive attack to these estimation tasks goes through the following ritual: you get your hands on the empirical distribution, you plug it in \(F\) and you hope for the best. Turns out, you are out of luck if the function \(f\) is non-smooth and in these cases you end up with a suboptimal estimator. Previous works have also looked at more sophisticated estimators (like the local moment matching or LMM and profile maximum likelihood or PML estimator). Turns out, using the LMM or PML estimator leads to optimal sample complexity for a handful of symmetric properties (as long as \(\varepsilon \geq n^{-1/3}\)). This paper considers the question of what can you say for supersmall values of \(\varepsilon\) where \(n^{-1/2} \leq \varepsilon \leq n^{-1/3}\). (The \(n^{-1/2}\) appears because there are estimators that use the knowledge of \(f\) and \(\varepsilon\) can be driven all the way down to \(n^{-1/2}\) for these estimators). The paper focuses on estimators which do not exploit any structure in \(f\). In particular, the paper specializes this question to PML and shows a fundamental limitation on PML which means that the PML approach fails to be sample optimal for the entire range of \(\varepsilon\) and is sample optimal only for \(\varepsilon >> n^{-1/3}\) — which also confirms a conjecture of Han and Shiragur (and refutes a conjecture of Acharya et al. who postulated this is sample optimal for the entire range of \(\varepsilon\)).

\(k\)-Forrelation Optimally Separates Quantum and Classical Query
Complexity
, by Nikhil Bansal and Makrand Sinha (arXiv). Understanding the power of quantum query over classical queries is a well motivated problem with a rich history. One of the biggest questions in this area asks for the largest separation between classical and quantum query complexities. In a breakthrough, Aaronson and Ambainis [4] showed a fundamental simulation result which confirmed that you can simulate \(q\) quantum queries with \(O(N^{1 – 1/2q})\) classical queries in the randomized decision tree model of computation as long as \(q = O(1)\). In the same paper, the authors also showed that the standard forrelation* problem exhibits a \(1\) versus \(\widetilde{\Omega}(\sqrt n)\) separation. This means that for \(q = 1\), you essentially have optimal separation. But what about \(q > 1\)? To this end, Aaronson and Ambainis conjectured that a certain problem which they called \(k\)-forrelation — which can be computed with \(q = k/2\) queries requires at least \(\widetilde{\Omega}(n^{1-1/k})\) classical queries. The current work precisely confirms this conjecture.

(*) The forrelation problem asks you to decide whether one Boolean function is highly correlated with the Fourier transform of a second function.

(Edit: Added Later) Simon Apers points out a paper by Shrestov, Storozhenko and Wu that we missed. (Thanks Simon)! Here is a brief report on that paper.

An optimal separation of randomized and quantum query complexity (by Alexander Shrestov, Andrey Storozhenko and Pei Wu)(arXiv) Similar to the paper by Bansal and Sinha [BS20] mentioned above, this paper also resolves the conjecture by Aaronson and Ambainis proving the same result. Like the paper also notes, the techniques in both of these works are completely different and incomparable. On the one hand [BS20] proves the separation for an explicit function as opposed to a function chosen uniformly at random from a certain set as considered in this work. On the other hand, the separation result shown in [BS20] only applies when the query algorithm returns the correct answer with probability at least \(1/2 + 1/poly(\log n)\) — in contrast the results in this paper apply even when the query algorithm is required to have probability of correctness be a constant at least \(1/2\). In addition, this work also proves the \(\ell\)-Fourier weight conjecture of Tal which is of independent interest beyond quantum computing.

So, it looks like all in all we had a great month with two concurrent papers both resolving Aaronson Ambainis conjecture (yet again after two concurrent papers on testing Hamiltonicity)!

References:

[1] Noga Alon, Eldar Fischer, Michael Krivelevich, and Mario Szegedy. Efficient testing of
large graphs. Combinatorica, 20(4):451–476, 2000.


[2] Oded Goldreich. On testing hamiltonicity in the bounded degree graph model. Electronic Colloquium on Computational Complexity (ECCC), (18), 2020

[3] Eric Blais, Joshua Brody, and Kevin Matulef. Property testing lower bounds via communication complexity. CCC 2011

[4] Scott Aaronson and Andris Ambainis. Forrelation: A problem that optimally separates quantum from classical computing. SIAM J. Comput., 47(3):982–1038, 2018

News for May 2020

Last month saw activity across a diverse collection of topics in sublinear algorithms. In particular, we had the following five six papers. (Sorry, I missed one)

One-Sided Error Testing of Monomials and Affine Subspaces by Oded Goldreich and Dana Ron (ECCC). This work focuses on one-sided testing of two kinds of problems (and their variants):
1. Testing Monomials: Suppose you are given a function \(f \colon \{0,1\}^n \to \{0,1\}\). Is \(f = \wedge_{i \in I} x_i\) (that is, is \(f\) a monotone monomial).
2. Testing Affine Subspaces: Consider the task of testing whether a \(f \colon \mathcal{F}^n \to \{0,1\}\) is the indicator of an \((n-k)\)-dimensional affine space for some \(k\) (where \(\mathcal{F}\) is a finite field).

The paper shows that the general problem — the one in which the arity of the monomial (resp the co-dimension of the subspace) is not specified has one-sided query complexity \(\widetilde{O}(1/\varepsilon)\). The same holds for testing whether the arity of the monomial is at most \(k\) (resp the co-dimension of the subspace is at most \(k\)). Finally, the exact problem which seeks to test whether the arity of the monomial is exactly \(k\) (resp the co-dimension of the space is exactly \(k\)) has query complexity \(\Omega(\log n)\). For two sided testers however, size oblivious testers are known for this problem. Thus, like the authors remark, two-sided error is inherent in the case of the exact version of the problem.

Sampling Arbitrary Subgraphs Exactly Uniformly in Sublinear Time by Hendrik Fichtenberger, Mingze Gao, Pan Peng (arXiv). Readers of PT Review are no strangers to the problem of counting cliques in sublinear time (with a certain query model). Building on tools from [1], in [2], Eden-Ron-Seshadhri gave the first algorithms for counting number of copies \(K_r\) in a graph \(G\) to within a \((1 \pm \varepsilon)\) multiplicative factor. En route to this result, they also gave a procedure to sample cliques incident to some special set \(S \subseteq V(G)\). The query model in [2] allowed the following queries: a u.a.r vertex query, degree query, \(i^{th}\) neighbor query and a pair query which answers whether a pair \((u,v)\) forms an edge. The work under consideration shows a result which I personally find remarkable: given the additional ability to get a u.a.r edge sample, we can do the following. For any graph \(H\) we can obtain a uniformly random subgraph isomorphic to \(H\) in \(G\). Let that sink in: this work shows that you can sample \(H\) exactly uniformly from the graph \(G\).

Finding Planted Cliques in Sublinear Time by Jay Mardia, Hilal Asi, Kabir Aladin Chandrasekher (arXiv). Planted Clique is a time honored problem in average case complexity. This classic problem asks the following: You are given a \(G \sim \mathcal{G}(n, 1/2)\). Suppose I select a subset of \(k\) vertices in this graph and put a clique on the subgraph they induce. In principle it is possible to recover the clique I planted if \(k > (2 + \varepsilon) \log n\). But it seems you get polynomial time algorithms only when \(k \geq \Omega(\sqrt n)\) even after you throw SDPs at the problem. Moreover, so far, the algorithms which recover the planted \(k\)-clique were known to take \(\widetilde{O}(n^2)\) time. This work shows that you actually get algorithms which take time \(\widetilde{O}(n^{3/2})\) if \(k \geq \Omega(\sqrt{n \log n})\). The key idea is to first obtain a “core” part of the clique of size \(O(\log n)\) in time \(\widetilde{O}(n^2/k)\). This is followed up with a clique completion routine where you mark all vertices connected to the entire core as being potentially in the clique. The paper also shows a conditional lower bound result which shows that given query access to adjacency matrix of the graph, a natural family of non-adaptive algorithms cannot recover a planted \(k\) clique in time \(o\left(\frac{n}{k}\right)^3\) (for \(k \geq \widetilde{\Omega}(\sqrt n))\).

A robust multi-dimensional sparse Fourier transform in the continuous setting by Yaonan Jin, Daogao Liu and Zhao Song (arXiv). Suppose you are given an unknown signal whose Fourier Spectrum is k-sparse (that is, there are at most k dominant Fourier Coefficients and all the others are zero or close to zero). Significant research effort has been devoted to learn these signals leading to works which study this problem for multi-dimensional discrete setting and in the one-dimensional continuous case. The \(d\)-dimensional continuous case \((d = \Theta(1))\) was largely unexplored. This work makes progress on this frontier by making some natural assumptions on the unknown signal. In particular, the paper assumes that the frequencies — which are vectors \(f_i’s \in R^d\) — are well separated and satisfy \(\|f_i – f_j\|_2 \leq \eta\) and that all \({f_i}_{i \in [k]} \subseteq [-F, F]^d\) sit inside a bounded box.
The authors assume sample access to the signal in the sense that at any desired timestep \(\tau\), the algorithm can sample the signal’s value. With this setup, the authors show that all the dominant frequencies can be recovered with a \(O_d(k \cdot \log(F/\eta))\) samples by considering a relatively small time horizon.

Extrapolating the profile of a finite population by Soham Jana, Yury Polyanskiy, Yihong Wu (arXiv). Consider the following setup. You are given a universe \(k\) balls. Ball come in up to \(k\) different colors. Say you \(\theta_j\) balls in color \(j\) for each \(j \in [k]\). One of the fundamental problems in statistics considers taking samples \(m\) balls from the universe and attempts estimating “population profile” (that is, the number of balls in each color). Historically, it is known that unless an overwhelming majority of the universe has been seen, one cannot estimate the empirical distribution of colors. This paper shows that in the sublinear regime, with \(m \geq \omega(k/\log k)\), it is possible to consistently estimate the population profile in total variation. And once you have a handle on the empirical distribution of the population, you can go ahead and learn lots of interesting label invariant properties of your universe (things like entropy, number of distinct elements etc).

(Edit added later)

Testing Positive Semi-Definiteness via Random Submatrices by Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram (arXiv). Suppose I give you a PSD matrix \(A \in R^{n \times n}\). You know that all of its principle submatrices are also PSD. What if \(A\) was \(\varepsilon\)-far from the PSD cone (in a sense I will define soon)? What can you say about the eigenvalues of principle submatrices of \(A\) now? In this paper, the authors tackle precisely this question. The paper defines a matrix \(A\) to be \(\varepsilon\)-far in \(\ell_2^2\) distance from the PSD Cone if you have that \(\min_{B \geq 0: B \in R^{n \times n}}\|A – B\|_F^2 \geq \varepsilon n^2\). You are allowed to randomly sample a bunch of principle submatrices (of order roughly \(O(1/\varepsilon)\) by \(O(1/\varepsilon)\) and check if they are PSD. Armed with this setup, the paper gives a non-adaptive one sided tester for this problem which makes \(\widetilde{O}(1/\varepsilon^4)\) queries. The paper also supplements this result with a lower bound of \(\widetilde{\Omega}(1/\varepsilon^2)\) queries.

If I missed something, please let me know. This is my first post on PT Review and I might have botched up a few things.

References

[1] Talya Eden, Amit Levi, Dana Ron and C. Seshadhri. Approximately Counting Triangles in Sublinear Time. 56th Annual Symposium on Foundations of Computer Science, 2015

[2] Talya Eden, Dana Ron and C. Seshadhri. On approximating the number of k-cliques in sublinear time. Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing 2018.