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Block sensitivity ($bs(f)$), certificate complexity ($C(f)$) and fractional certificate complexity ($C^*(f)$) are three fundamental combinatorial measures of complexity of a boolean function $f$. It has long been known that $bs(f) leq C^{ast}(f) leq C(f) =O(bs(f)^2)$. We provide an infinite family of examples for which $C(f)$ grows quadratically in $C^{ast}(f)$ (and also $bs(f)$) giving optimal separations between these measures. Previously the biggest separation known was $C(f)=C^{ast}(f)^{log_{4.5}5}$. We also give a family of examples for which $C^{ast}(f)=Omega(bs(f)^{3/2})$. These examples are obtained by composing boolean functions in various ways. Here the composition $f circ g$ of $f$ with $g$ is obtained by substituting for each variable of $f$ a copy of $g$ on disjoint sets of variables. To construct and analyse these examples we systematically investigate the behaviour under function composition of these measures and also the sensitivity measure $s(f)$. The measures $s(f)$, $C(f)$ and $C^{ast}(f)$ behave nicely under composition: they are submultiplicative (where measure $m$ is submultiplicative if $m(f circ g) leq m(f)m(g)$) with equality holding under some fairly general conditions. The measure $bs(f)$ is qualitatively different: it is not submultiplicative. This qualitative difference was not noticed in the previous literature and we correct some errors that appeared in previous papers. We define the composition limit of a measure $m$ at function $f$, $m^{lim}(f)$ to be the limit as $k$ grows of $m(f^{(k)})^{1/k}$, where $f^{(k)}$ is the iterated composition of $f$ with itself $k$-times. For any function $f$ we show that $bs^{lim}(f) = (C^*)^{lim}(f)$ and characterize $s^{lim}(f), (C^*)^{lim}(f)$, and $C^{lim}(f)$ in terms of the largest eigenvalue of a certain set of $2times 2$ matrices associated with $f$.
We study Boolean circuits as a representation of Boolean functions and consider different equivalence, audit, and enumeration problems. For a number of restricted sets of gate types (bases) we obtain efficient algorithms, while for all other gate types we show these problems are at least NP-hard.
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
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
The main result of this paper is a generalization of the classical blossom algorithm for finding perfect matchings. Our algorithm can efficiently solve Boolean CSPs where each variable appears in exactly two constraints (we call it edge CSP) and all
Sensitivity conjecture is a longstanding and fundamental open problem in the area of complexity measures of Boolean functions and decision tree complexity. The conjecture postulates that the maximum sensitivity of a Boolean function is polynomially r