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Register a new userQuadratically Tight Relations for Randomized Query Complexity
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Srijita Kundu
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Let $f:{0,1}^n rightarrow {0,1}$ be a Boolean function. The certificate complexity $C(f)$ is a complexity measure that is quadratically tight for the zero-error randomized query complexity $R_0(f)$: $C(f) leq R_0(f) leq C(f)^2$. In this paper we study a new complexity measure that we call expectational certificate complexity $EC(f)$, which is also a quadratically tight bound on $R_0(f)$: $EC(f) leq R_0(f) = O(EC(f)^2)$. We prove that $EC(f) leq C(f) leq EC(f)^2$ and show that there is a quadratic separation between the two, thus $EC(f)$ gives a tighter upper bound for $R_0(f)$. The measure is also related to the fractional certificate complexity $FC(f)$ as follows: $FC(f) leq EC(f) = O(FC(f)^{3/2})$. This also connects to an open question by Aaronson whether $FC(f)$ is a quadratically tight bound for $R_0(f)$, as $EC(f)$ is in fact a relaxation of $FC(f)$. In the second part of the work, we upper bound the distributed query complexity $D^mu_epsilon(f)$ for product distributions $mu$ by the square of the query corruption bound ($mathrm{corr}_epsilon(f)$) which improves upon a result of Harsha, Jain and Radhakrishnan [2015]. A similar statement for communication complexity is open.
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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 complexi
We prove two new results about the randomized query complexity of composed functions. First, we show that the randomized composition conjecture is false: there are families of partial Boolean functions $f$ and $g$ such that $R(fcirc g)ll R(f) R(g)$. In fact, we show that the left hand side can be polynomially smaller than the right hand side (though in our construction, both sides are polylogarithmic in the input size of $f$). Second, we show that for all $f$ and $g$, $R(fcirc g)=Omega(mathop{noisyR}(f)cdot R(g))$, where $mathop{noisyR}(f)$ is a measure describing the cost of computing $f$ on noisy oracle inputs. We show that this composition theorem is the strongest possible of its type: for any measure $M(cdot)$ satisfying $R(fcirc g)=Omega(M(f)R(g))$ for all $f$ and $g$, it must hold that $mathop{noisyR}(f)=Omega(M(f))$ for all $f$. We also give a clean characterization of the measure $mathop{noisyR}(f)$: it satisfies $mathop{noisyR}(f)=Theta(R(fcirc gapmaj_n)/R(gapmaj_n))$, where $n$ is the input size of $f$ and $gapmaj_n$ is the $sqrt{n}$-gap majority function on $n$ bits.
Let the randomized query complexity of a relation for error probability $epsilon$ be denoted by $R_epsilon(cdot)$. We prove that for any relation $f subseteq {0,1}^n times mathcal{R}$ and Boolean function $g:{0,1}^m rightarrow {0,1}$, $R_{1/3}(fcirc g^n) = Omega(R_{4/9}(f)cdot R_{1/2-1/n^4}(g))$, where $f circ g^n$ is the relation obtained by composing $f$ and $g$. We also show that $R_{1/3}left(f circ left(g^oplus_{O(log n)}right)^nright)=Omega(log n cdot R_{4/9}(f) cdot R_{1/3}(g))$, where $g^oplus_{O(log n)}$ is the function obtained by composing the xor function on $O(log n)$ bits and $g^t$.
Let $R_epsilon(cdot)$ stand for the bounded-error randomized query complexity with error $epsilon > 0$. For any relation $f subseteq {0,1}^n times S$ and partial Boolean function $g subseteq {0,1}^m times {0,1}$, we show that $R_{1/3}(f circ g^n) in Omega(R_{4/9}(f) cdot sqrt{R_{1/3}(g)})$, where $f circ g^n subseteq ({0,1}^m)^n times S$ is the composition of $f$ and $g$. We give an example of a relation $f$ and partial Boolean function $g$ for which this lower bound is tight. We prove our composition theorem by introducing a new complexity measure, the max conflict complexity $bar chi(g)$ of a partial Boolean function $g$. We show $bar chi(g) in Omega(sqrt{R_{1/3}(g)})$ for any (partial) function $g$ and $R_{1/3}(f circ g^n) in Omega(R_{4/9}(f) cdot bar chi(g))$; these two bounds imply our composition result. We further show that $bar chi(g)$ is always at least as large as the sabotage complexity of $g$, introduced by Ben-David and Kothari.
We study the composition question for bounded-error randomized query complexity: Is R(f o g) = Omega(R(f) R(g)) for all Boolean functions f and g? We show that inserting a simple Boolean function h, whose query complexity is only Theta(log R(g)), in between f and g allows us to prove R(f o h o g) = Omega(R(f) R(h) R(g)). We prove this using a new lower bound measure for randomized query complexity we call randomized sabotage complexity, RS(f). Randomized sabotage complexity has several desirable properties, such as a perfect composition theorem, RS(f o g) >= RS(f) RS(g), and a composition theorem with randomized query complexity, R(f o g) = Omega(R(f) RS(g)). It is also a quadratically tight lower bound for total functions and can be quadratically superior to the partition bound, the best known general lower bound for randomized query complexity. Using this technique we also show implications for lifting theorems in communication complexity. We show that a general lifting theorem for zero-error randomized protocols implies a general lifting theorem for bounded-error protocols.
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