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arXiv:1512.01968 · Leibniz international proceedings in informatics

Partition Bound Is Quadratically Tight for Product Distributions

Prahladh Harsha, Rahul Jain, Jaikumar Radhakrishnan

Published Dec 7, 2015• 1 citation
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Abstract

Let f: {0,1}^n*{0,1}^n -> {0,1} be a 2-party function. For every product distribution mu on {0,1}^n*{0,1}^n, we show that CC^{mu}_{0.49}(f) = O(log(prt_{1/8}(f))*log(log(prt_{1/8}(f)))^2), where CC^{mu}_{epsilon}(f) is the distributional communication complexity of f with error at most epsilon under the distribution mu and prt_{1/8}(f) is the partition bound of f, as defined by Jain and Klauck [Proc. 25th CCC, 2010]. We also prove a similar bound in terms of IC_{1/8}(f), the information complexity of f, namely, CC^{mu}_{0.49}(f) = O((IC_{1/8}(f)*log(IC_{1/8}(f)))^2). The latter bound was recently and independently established by Kol [Proc. 48th STOC, 2016] using a different technique. We show a similar result for query complexity under product distributions. Let g: {0,1}^n -> {0,1} be a function. For every bit-wise product distribution mu on {0,1}^n, we show that QC^{mu}_{0.49}(g) = O((log(qprt_{1/8}(g))*log(log(qprt_{1/8}(g))))^2), where QC^{mu}_{epsilon}(g) is the distributional query complexity of f with error at most epsilon under the distribution mu and qprt_{1/8}(g) is the query partition bound of the function g. Partition bounds were introduced (in both communication complexity and query complexity models) to provide LP-based lower bounds for randomized communication complexity and randomized query complexity. Our results demonstrate that these lower bounds are polynomially tight for product distributions.

DOI
10.4230/lipics.icalp.2016.135

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Let f: {0,1}^n*{0,1}^n -> {0,1} be a 2-party function. For every product distribution mu on {0,1}^n*{0,1}^n, we show that CC^{mu}{0.49}(f) = O(log(prt{1/8}(f))*log(log(prt_{1/8}(f)))^2), where CC^{mu}{epsilon}(f) is the distributional communication complexity of f with error at most epsilon under the distribution mu and prt{1/8}(f) is the partition bound of f, as defined by Jain and Klauck [Proc. 25th CCC, 2010]. We also prove a similar bound in terms of IC_{1/8}(f), the information complexity of f, namely, CC^{mu}{0.49}(f) = O((IC{1/8}(f)*log(IC_{1/8}(f)))^2). The latter bound was recently and independently established by Kol [Proc. 48th STOC, 2016] using a different technique. We show a similar result for query complexity under product distributions. Let g: {0,1}^n -> {0,1} be a function. For every bit-wise product distribution mu on {0,1}^n, we show that QC^{mu}{0.49}(g) = O((log(qprt{1/8}(g))*log(log(qprt_{1/8}(g))))^2), where QC^{mu}{epsilon}(g) is the distributional query complexity of f with error at most epsilon under the distribution mu and qprt{1/8}(g) is the query partition bound of the function g. Partition bounds were introduced (in both communication complexity and query complexity models) to provide LP-based lower bounds for randomized communication complexity and randomized query complexity. Our results demonstrate that these lower bounds are polynomially tight for product distributions.
In this work we introduce, both for classical communication complexity and query complexity, a modification of the 'partition bound' introduced by Jain and Klauck [2010]. We call it the 'public-coin partition bound'. We show that (the logarithm to the base two of) its communication complexity and query complexity versions form, for all relations, a quadratically tight lower bound on the public-coin randomized communication complexity and randomized query complexity respectively.
We show two new direct product results in two different models of communication complexity. Our first result is in the one-way public-coin model. Let f ⊆ X × Y × Z be a relation and ϵ > 0 be a constant. Let R 1,pub ϵ ( f ) represent the communication complexity of f , with worst-case error ϵ in this model. We show that if for computing f k ( k independent copies of f ) in this model, o ( k ċ R 1, pub 1/3 ( f )) communication is used, then the success is exponentially small in k . We show a new tight characterization of communication complexity in this model which strengthens the tight characterization shown in Jain et al. [2008]. We use this new characterization to show our direct product result and this characterization may also be of independent interest. Our second direct product result is in the model of two-way public-coin communication complexity. We show a direct product result for all relations in this model in terms of a new complexity measure that we define. Our new measure is a generalization to nonproduct distributions, of the two-way product subdistribution bound of Jain et al. [2008]. Our direct product result therefore generalizes to nonproduct distributions, their direct product result in terms of the two-way product subdistribution bound. As an application of our new direct product result, we reproduce (via completely different arguments) strong direct product result for the set-disjointness problem which was previously shown by Klauck [2010]. We show this by proving that our new complexity measure gives a tight lower bound of Ω( n ) for the set-disjointness problem on n -bit inputs (this strengthens the linear lower bound on the rectangle/corruption bound for set-disjointness shown by Razborov [1992]). In addition, we show that many previously known direct product results in this model are uniformly implied and often strengthened by our result.
We present a linear program for a one-way version of the partition bound of Jain and Klauck, denoted prtε1(f)\mathsf{prt}^1_\varepsilon(f)prtε1​(f), and show that it characterizes the one-way randomized communication complexity Rε1(f)\mathsf{R}_\varepsilon^1(f)Rε1​(f) with shared randomness and worst-case error ε\varepsilonε of any relation f⊆X×Y×Zf\subseteq\mathcal{X}\times\mathcal{Y}\times\mathcal{Z}f⊆X×Y×Z. More precisely, for all ε,δ∈(0,1/2)\varepsilon,δ\in(0,1/2)ε,δ∈(0,1/2), Rε1(f)≥log⁡prtε1(f)\mathsf{R}_\varepsilon^1(f) \geq \log\mathsf{prt}_\varepsilon^1(f)Rε1​(f)≥logprtε1​(f) and Rε+δ1(f)≤⌈log⁡prtε1(f)+log⁡log⁡(1/δ)⌉\mathsf{R}_{\varepsilon+δ}^1(f) \leq \lceil\log\mathsf{prt}_\varepsilon^1(f) + \log\log(1/δ)\rceilRε+δ1​(f)≤⌈logprtε1​(f)+loglog(1/δ)⌉. This improves upon the characterization of Rε1(f)\mathsf{R}_\varepsilon^1(f)Rε1​(f) in terms of the rectangle bound due to Jain, Klauck, and Nayak [STOC'08], which is tight only up to an additive O(log⁡(1/δ))O(\log(1/δ))O(log(1/δ)) term.
We show two new direct product results in two different models of communication complexity. Our first result is in the one-way public-coin model. Let f ⊆ X × Y × Z be a relation and e > 0 be a constant. Let R1,pube(f) represent the communication complexity of f, with worst-case error e in this model. We show that if for computing fk (k independent copies of f) in this model, o(k c R1, pub1/3(f)) communication is used, then the success is exponentially small in k. We show a new tight characterization of communication complexity in this model which strengthens the tight characterization shown in Jain et al. [2008]. We use this new characterization to show our direct product result and this characterization may also be of independent interest.Our second direct product result is in the model of two-way public-coin communication complexity. We show a direct product result for all relations in this model in terms of a new complexity measure that we define. Our new measure is a generalization to nonproduct distributions, of the two-way product subdistribution bound of Jain et al. [2008]. Our direct product result therefore generalizes to nonproduct distributions, their direct product result in terms of the two-way product subdistribution bound. As an application of our new direct product result, we reproduce (via completely different arguments) strong direct product result for the set-disjointness problem which was previously shown by Klauck [2010]. We show this by proving that our new complexity measure gives a tight lower bound of Ω(n) for the set-disjointness problem on n-bit inputs (this strengthens the linear lower bound on the rectangle/corruption bound for set-disjointness shown by Razborov [1992]). In addition, we show that many previously known direct product results in this model are uniformly implied and often strengthened by our result.
The "direct product problem" is a fundamental question in complexity theory which seeks to understand how the difficulty of computing a function on each of k independent inputs scales with k. We prove the following direct product theorem (DPT) for query complexity: if every T-query algorithm has success probability at most 1 - ε in computing the Boolean function f on input distribution μ, then for α ≤ 1, every αεTk-query algorithm has success probability at most (2αε(1 - ε))kin computing the fc-fold direct product f⊗kcorrectly on k independent inputs from μ. In light of examples due to Shaltiel, this statement gives an essentially optimal tradeoff between the query bound and the error probability. Using this DPT, we show that for an absolute constant α >; 0, the worst-case success probability of any αR2(f)k-query randomized algorithm for f⊗kfalls exponentially with k. The best previous statement of this type, due to Klauck, Spalek, and de Wolf, required a query bound of O(bs(f)k). Our proof technique involves defining and analyzing a collec tion of martingales associated with an algorithm attempting to solve f⊗k. Our method is quite general and yields a new XOR lemma and threshold DPT for the query model, as well as DPTs for the query complexity of learning tasks, search problems, and tasks involving interaction with dynamic entities. We also give a version of our DPT in which decision tree size is the resource of interest.
We describe new lower bounds for randomized communication complexity and query complexity which we call the partition bounds. They are expressed as the optimum value of linear programs. For communication complexity we show that the partition bound is stronger than both the rectangle/corruption bound and the γ2/generalized discrepancy bounds. In the model of query complexity we show that the partition bound is stronger than the approximate polynomial degree and classical adversary bounds. We also exhibit an example where the partition bound is quadratically larger than the approximate polynomial degree and adversary bounds.