Property Testing Lower Bounds via Communication Complexity

We develop a new technique for proving lower bounds in property testing, by showing a strong connection between testing and communication complexity. We give a simple scheme for reducing communication problems to testing problems, thus allowing us to use known lower bounds in communication complexity to prove lower bounds in testing. This scheme is general and implies a number of new testing bounds, as well as simpler proofs of several known bounds. For the problem of testing whether a boolean function is k-linear (a parity function on k variables), we achieve a lower bound of Ω(k) queries, even for adaptive algorithms with two-sided error, thus confirming a conjecture of Goldreich (2010). The same argument behind this lower bound also implies a new proof of known lower bounds for testing related classes such as k-juntas. For some classes, such as the class of monotone functions and the class of s-sparse GF(2) polynomials, we significantly strengthen the best known bounds.

A few years ago, Blais, Brody, and Matulef: Comput. Complex. 21(2), 311–358 (2012) presented a methodology for proving lower bounds for property testing problems by reducing them from problems in communication complexity. Recently, Bhrushundi, Chakraborty, and Kulkarni (2014) showed that some reductions of this type can be deconstructed to two separate reductions, from communication complexity to randomized parity decision trees and from the latter to property testing. This work follows up on these ideas. We introduce a model called linear-access algorithms, which is a generalization of randomized parity decision trees, and show several methods to reduce communication complexity problems to problems for linear-access algorithms and problems for linear-access algorithms to property testing problems. This approach yields a new interpretation for several well-known reductions, since we present these reductions as a composition of two steps with fundamentally different functionalities. Furthermore, we demonstrate the potential of proving lower bounds on property testing problems by reducing them directly from problems for linear-access algorithms. In particular, we provide an alternative and simple proof for a known lower bound of Ω(k) queries on testing “k-linearity”; that is, the property of k-sparse linear functions over $\mathbb {F}_{2}$ . This alternative proof relies on a theorem by Linial and Samorodnitsky: Combinatorica 22(4), 497–522 (2002). We then extend this result to a new lower bound of Ω(s) queries for testing s-sparse degree-d polynomials over $\mathbb {F}_{2}$ , for any $d\in \mathbb {N}$ . In addition we provide a simple proof for the hardness of testing some families of linear subcodes.

A direct sum theorem for two parties and a function f states that the communication cost of solving k copies of f simultaneously with error probability 1/3 is at least k · R1/3(f), where R1/3(f) is the communication required to solve a single copy of f with error probability 1/3. We improve this for a natural family of functions f, showing that the 1-way communication required to solve k copies of f simultaneously with probability 2/3 is Ω(k · R1/k(f)). Since R1/k (f) may be as large as Ω(R1/3(f) · log k), we asymptotically beat the direct sum bound for such functions, showing that the trivial upper bound of solving each of the k copies of f with probability 1 − O(1/k) and taking a union bound is optimal! In order to achieve this, our direct sum involves a novel measure of information cost which allows a protocol to abort with constant probability, and otherwise must be correct with very high probability. Moreover, for the functions considered, we show strong lower bounds on the communication cost of protocols with these relaxed guarantees; indeed, our lower bounds match those for protocols that are not allowed to abort. In the distributed and streaming models, where one wants to be correct not only on a single query, but simultaneously on a sequence of n queries, we obtain optimal lower bounds on the communication or space complexity. Lower bounds obtained from our direct sum result show that a number of techniques in the sketching literature are optimal, including the following: (JL transform) Lower bound of on the dimension of (oblivious) Johnson-Lindenstrauss transforms. (ℓp-estimation) Lower bound for the size of encodings of n vectors in [±M]d that allow ℓ1 or ℓ2-estimation of . (Matrix sketching) Lower bound of on the dimension of a matrix sketch S satisfying the entrywise guarantee |(ASST B)i,j − (AB)i,j | ≤ ε‖Ai‖2‖Bj‖2. (Database joins) Lower bound of for sketching frequency vectors of n tables in a database, each with M records, in order to allow join size estimation.

Symmetric polynomials over [formula omitted] and simultaneous communication protocols

Karchmer, Raz, and Wigderson (1995) discuss the circuit depth complexity of n-bit Boolean functions constructed by composing up to d = log n/log log n levels of k = log n-bit Boolean functions. Any such function is in AC1 . They conjecture that circuit depth is additive under composition, which would imply that any (bounded fan-in) circuit for this problem requires $ dk \in \Omega({\rm log}^2 n/{\rm log\,log}\,n) $ depth. This would separate AC1 from NC1. They recommend using the communication game characterization of circuit depth. In order to develop techniques for using communication complexity to prove circuit depth lower bounds, they suggest an intermediate communication complexity problem which they call the Universal Composition Relation. We give an almost optimal lower bound of dk—O(d 2(k log k)1/2) for this problem. In addition, we present a proof, directly in terms of communication complexity, that there is a function on k bits requiring $ \Omega(k) $ circuit depth. Although this fact can be easily established using a counting argument, we hope that the ideas in our proof will be incorporated more easily into subsequent arguments which use communication complexity to prove circuit depth bounds.

We show that if Arthur-Merlin protocols can be derandomized, then there is a Boolean function computable in deterministic exponential-time with access to an $\bm{\NP}$ oracle, that cannot be computed by Boolean circuits of {\em exponential} size. More formally, if $\bm{\prAM \subseteq \P^{\NP}}$ then there is a Boolean function in $\bm{\E^{\NP}}$ that requires circuits of size $\bm{2^{\Omega(n)}}$. $\bm{\prAM}$ is the class of promise problems that have Arthur-Merlin protocols, $\bm{\P^{\NP}}$ is the class of functions that can be computed in deterministic polynomial-time with an $\bm{\NP}$ oracle and $\bm{\E^{\NP}}$ is its exponential analogue. The lower bound in the conclusion of our theorem suffices to construct very strong pseudorandom generators. We also show that the same conclusion holds if the problem of approximate counting the number of accepting paths of a nondeterministic Turing machine up to multiplicative factors can be done in nondeterministic polynomial-time. In other words, showing nondeterministic fully polynomial-time approximation schemes for $\bm{\sharp \P}$-complete problems require proving exponential-size circuit lower bounds. A few works have already shown that if we can find efficient deterministic solutions to some specific tasks (or classes) that are known to be solvable efficiently by randomized algorithms (or proofs), then we obtain lower bounds against certain circuit models. These lower bounds were only with respect to polynomial-size circuits {\em even if full derandomization is assumed}. Thus they only implied fairly weak pseudorandom generators (if at all). A key ingredient in our proof is a connection between computational learning theory and exponential-size lower bounds. We show that the existence of deterministic learning algorithms with certain properties implies exponential-size lower bounds, where the complexity of the hard function is related to the complexity of the learning algorithm.

In this paper we study the question: How useful is randomization in speeding up Exclusive Write PRAM computations? Our results give further evidence that randomization is of limited use in these types of computations. First we examine a compaction problem on both the CREW and EREW PRAM models, and we present randomized lower bounds which match the best deterministic lower bounds known. (For the CREW PRAM model, the lower bound is asymptotically optimal.) These are the first non-trivial randomized lower bounds known for the compaction problem on these models. We show that our lower bounds also apply to the problem of approximate compaction. Next we examine the problem of computing boolean functions on the CREW PRAM model, and we present a randomized lower bound, which improves on the previous best randomized lower bound for many boolean functions, including the OR function. (The previous lower bounds for these functions were asymptotically optimal, but we improve the constant multiplicative factor.) We also give an alternate proof for the randomized lower bound on PARITY, which was already optimal to within a constant additive factor. Lastly, we give a randomized lower bound for integer merging on an EREW PRAM which matches the best deterministic lower bound known. In all our proofs, we use the Random Adversary method, which has previously only been used for proving lower bounds on models with Concurrent Write capabilities. Thus this paper also serves to illustrate the power and generality of this method for proving parallel randomized lower bounds.

The nonnegative rank of an entrywise nonnegative matrix \(A \in \mathbb {R}^{m \times n}_+\) is the smallest integer \(r\) such that \(A\) can be written as \(A=UV\) where \(U \in \mathbb {R}^{m \times r}_+\) and \(V \in \mathbb {R}^{r \times n}_+\) are both nonnegative. The nonnegative rank arises in different areas such as combinatorial optimization and communication complexity. Computing this quantity is NP-hard in general and it is thus important to find efficient bounding techniques especially in the context of the aforementioned applications. In this paper we propose a new lower bound on the nonnegative rank which, unlike most existing lower bounds, does not solely rely on the matrix sparsity pattern and applies to nonnegative matrices with arbitrary support. The idea involves computing a certain nuclear norm with nonnegativity constraints which allows to lower bound the nonnegative rank, in the same way the standard nuclear norm gives lower bounds on the standard rank. Our lower bound is expressed as the solution of a copositive programming problem and can be relaxed to obtain polynomial-time computable lower bounds using semidefinite programming. We compare our lower bound with existing ones, and we show examples of matrices where our lower bound performs better than currently known ones.

In function inversion, we are given a function $f:[N]\rightarrow[N]$ , and want to prepare some advice of size $S$ , such that we can efficiently invert any image in time $T$ . This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower bounds. Investigation of this problem in the quantum setting was initiated by Nayebi, Aaronson, Belovs, and Trevisan (2015), who proved a lower bound of $ST^{2}=\tilde{\Omega}(N)$ for random permutations against classical advice, leaving open an intriguing possibility that Grover's search can be sped up to time $\tilde{O}(\sqrt{N/S})$ . Recent works by Hhan, Xagawa, and Yamakawa (2019), and Chung, Liao, and Qian (2019) extended the argument for random functions and quantum advice, but the lower bound remains $ST^{2}=\tilde{\Omega}(N)$ . In this work, we prove that even with quantum advice, $ST+ T^{2}=\tilde{\Omega}(N)$ , is required for an algorithm to invert random functions. This demonstrates that Grover's search is optimal for $S=\tilde{O}(\sqrt{N})$ , ruling out any substantial speed-up for Grover's search even with quantum advice. Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019). To prove this result, we develop a general framework for establishing quantum time-space lower bounds. We further demonstrate the power of our framework by proving the following results. •Yao's box problem: We prove a tight quantum time-space lower bound for classical advice. For quantum advice, we prove a first time-space lower bound using shadow tomography. These results resolve two open problems posted by Nayebi et al (2015). •Salted cryptography: We show that “salting generically provably defeats preprocessing,” a result shown by Coretti, Dodis, Guo, and Steinberger (2018), also holds in the quantum setting. In particular, we prove quantum time-space lower bounds for a wide class of salted cryptographic primitives in the quantum random oracle model. This yields the first quantum time-space lower bound for salted collision-finding, which in turn implies that $\text{PWPP}^{\mathcal{O}} \nsubseteq \text{FBQP}^{\mathcal{O}}/\text{qpoly}$ relative to a random oracle $\mathcal{O}$ .

The primary problem in property testing is to decide whether a given function satisfies a certain property, or is far from any function satisfying it. This crucially requires a notion of distance between functions. The most prevalent notion is the Hamming distance over the {\em uniform} distribution on the domain. This restriction to uniformity is more a matter of convenience than of necessity, and it is important to investigate distances induced by more general distributions. In this paper, we make significant strides in this direction. We give simple and optimal testers for {\em bounded derivative properties} over {\em arbitrary product distributions}. Bounded derivative properties include fundamental properties such as monotonicity and Lipschitz continuity. Our results subsume almost all known results (upper and lower bounds) on monotonicity and Lipschitz testing. We prove an intimate connection between bounded derivative property testing and binary search trees (BSTs). We exhibit a tester whose query complexity is the sum of expected depths of optimal BSTs for each marginal. Furthermore, we show this sum-of-depths is also a lower bound. A fundamental technical contribution of this work is an {\em optimal dimension reduction theorem} for all bounded derivative properties, which relates the distance of a function from the property to the distance of restrictions of the function to random lines. Such a theorem has been elusive even for monotonicity for the past 15 years, and our theorem is an exponential improvement to the previous best known result.

We solve two continuous extremal problems on the classes of monotone functions: in the first problem we find extremal values for a line integral of a coordinate-wise monotone function of two variables from a rearrangement-invariant class of functions; in the second one we find extremal values for the expectation of a random process with monotone trajectories at a random time. In both cases we reduce the continuous problems to their discrete counterparts. The obtained discrete problems are on the one hand interesting on their own, and on the other hand give a natural explanation of the structure of the extremal functions for the continuous problems.

Motivated by the increasing need to understand the distributed algorithmic foundations of large-scale graph computations, we study some fundamental graph problems in a message-passing model for distributed computing where $k \geq 2$ machines jointly perform computations on graphs with n nodes (typically, $n \gg k$). The input graph is assumed to be initially randomly partitioned among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication \em rounds of the computation. Our main contribution is the \em General Lower Bound Theorem, a theorem that can be used to show non-trivial lower bounds on the round complexity of distributed large-scale data computations. The General Lower Bound Theorem is established via an information-theoretic approach that relates the round complexity to the minimal amount of information required by machines to solve the problem. Our approach is generic and this theorem can be used in a cookbook fashion to show distributed lower bounds in the context of several problems, including non-graph problems. We present two applications by showing (almost) tight lower bounds for the round complexity of two fundamental graph problems, namely \em PageRank computation and \em triangle enumeration. Our approach, as demonstrated in the case of PageRank, can yield tight lower bounds for problems (including, and especially, under a stochastic partition of the input) where communication complexity techniques are not obvious. Our approach, as demonstrated in the case of triangle enumeration, can yield stronger round lower bounds as well as message-round tradeoffs compared to approaches that use communication complexity techniques. We then present distributed algorithms for PageRank and triangle enumeration with a round complexity that (almost) matches the respective lower bounds; these algorithms exhibit a round complexity which scales superlinearly in k , improving significantly over previous results for these problems [Klauck et al., SODA 2015]. Specifically, we show the following results: \beginitemize item \em PageRank: We show a lower bound of $\tildeOmega (n/k^2)$ rounds, and present a distributed algorithm that computes the PageRank of all the nodes of a graph in $\tildeO (n/k^2)$ rounds. item \em Triangle enumeration: We show that there exist graphs with m edges where any distributed algorithm requires $\tildeOmega (m/k^5/3 )$ rounds. This result also implies the first non-trivial lower bound of $\tildeOmega(n^1/3 )$ rounds for the \em congested clique model, which is tight up to logarithmic factors. We then present a distributed algorithm that enumerates all the triangles of a graph in $\tildeO (m/k^5/3 + n/k^4/3 )$ rounds. \enditemize

We prove lower bounds on the randomized two-party communication complexity of functions that arise from read-once boolean formulae. A read-once boolean formula is a formula in propositional logic with the property that every variable appears exactly once. Such a formula can be represented by a tree, where the leaves correspond to variables, and the internal nodes are labeled by binary connectives. Under certain assumptions, this representation is unique. Thus, one can define the depth of a formula as the depth of the tree that represents it. The complexity of the evaluation of general read-once formulae, has attracted interest mainly in the decision tree model. In the communication complexity model, many interesting results deal with specific read-once formulae, such as DISJOINTNESS and TRIBES. In this paper we use information theory methods to prove lower bounds that hold for any read-once formula. Our lower bounds are of the form n(f)/c^{d(f)}, where n(f) is the number of variables and d(f) the depth of the formula, and they are optimal up to the constant c in the denominator.

In the $k$-party set disjointness problem, the goal is to determine whether given subsets $S_1,S_2,\dots,S_k\subseteq\{1,2,\dots,n\}$ have empty intersection. We study this problem in the number-on-the-forehead model of communication, where the $i$th party knows all the sets except for $S_i,$ and prove a lower bound of $\Omega(n/4^k)^{1/4}$ on the randomized and nondeterministic communication complexity. This lower bound is close to tight. Previous lower bounds for set disjointness with $k\geq3$ parties were weaker than $\Omega(n/2^{k^3})^{1/(k+1)}.$ We also prove that solving $\ell$ instances of set disjointness requires $\ell\cdot\Omega(n/4^k)^{1/4}$ bits of communication, even to achieve correctness probability exponentially close to $1/2.$ This gives the first direct product result for multiparty set disjointness, solving an open problem due to Beame et al. (2005). Finally, we construct a read-once $\{\wedge,\vee\}$-circuit of depth $3$ with exponentially small discrepancy for up to $k\approx\frac12\l...

Gap Hamming Distance is a well-studied problem in communication complexity, in which Alice and Bob have to decide whether the Hamming distance between their respective n-bit inputs is less than \(n/2-\sqrt{n}\) or greater than \(n/2+\sqrt{n}\). We show that every k-round bounded-error communication protocol for this problem sends a message of at least Ω(n/(k 2logk)) bits. This lower bound has an exponentially better dependence on the number of rounds than the previous best bound, due to Brody and Chakrabarti. Our communication lower bound implies strong space lower bounds on algorithms for a number of data stream computations, such as approximating the number of distinct elements in a stream.KeywordsCommunication ComplexityGap Hamming DistanceRound EliminationMeasure Concentration