Sensitivity, Affine Transforms and Quantum Communication Complexity

$\newcommand{\F}{\mathbb{F}}$We study the Boolean function parameters sensitivity ($s$), block sensitivity ($bs$), and alternation ($alt$) under specially designed affine transforms. For a function f:\F_2^n\to \{0,1\}, and $A=Mx+b$ for M \in \F_2^{n\times n} and b\in \F_2^n, the result of the transformation $g$ is defined as \forall x\in\F_2^n, g(x)=f(Mx+b). We study alternation under linear shifts ($M$ is the identity matrix) called the shift invariant alternation (denoted by $salt(f)$). We exhibit an explicit family of functions for which $salt(f)$ is $2^{\Omega(s(f))}$. We show an affine transform $A$, such that the corresponding function $g$ satisfies $bs(f,0^n) \le s(g)$, using which we proving that for $F(x,y)=f(x\land y)$, the bounded error quantum communication complexity of $F$ with prior entanglement, $Q^*_{1/3}(F)=\Omega(\sqrt{bs(f,0^n)})$. Our proof builds on ideas from Sherstov (2010) where we use specific properties of the above affine transformation. We show, * For a prime $p$ and $0<\epsilon<1$, any $f$ with $deg_p(f)\le(1-\epsilon)\log n$ must satisfy $Q^*_{1/3}(F) = \Omega(\frac{n^{\epsilon/2}}{\log n})$. Here, $deg_p(f)$ denotes the degree of the multilinear polynomial of $f$ over \F_p. * For any $f$ such that there exists primes $p$ and $q$ with $deg_q(f) \ge \Omega(deg_p(f)^\delta)$ for $\delta > 2$, the deterministic communication complexity - $D(F)$ and $Q^*_{1/3}(F)$ are polynomially related. In particular, this holds when $deg_p(f) = O(1)$. Thus, for this class of functions, this answers an open question (see Buhrman and deWolf (2001)) about the relation between the two measures. We construct linear transformation $A$, such that $g$ satisfies, $alt(f) \le 2s(g)+1$. Using this, we exhibit a family of Boolean functions that rule out a potential approach to settle the XOR Log-Rank conjecture via a proof of Sensitivity conjecture [Hao Huang (2019)].Comment: 19 pages, 1 figure. Added a new lower bound for shifted alternation (in Section 3) and an application to the existence of family of functions under linear transforms (in Section 5

Parity Decision Tree Complexity is Greater Than Granularity

We prove a new lower bound on the parity decision tree complexity $\mathsf{D}_{\oplus}(f)$ of a Boolean function $f$. Namely, granularity of the Boolean function $f$ is the smallest $k$ such that all Fourier coefficients of $f$ are integer multiples of $1/2^k$. We show that $\mathsf{D}_{\oplus}(f)\geq k+1$. This lower bound is an improvement of lower bounds through the sparsity of $f$ and through the degree of $f$ over $\mathbb{F}_2$. Using our lower bound we determine the exact parity decision tree complexity of several important Boolean functions including majority and recursive majority. For majority the complexity is $n - \mathsf{B}(n)+1$, where $\mathsf{B}(n)$ is the number of ones in the binary representation of $n$. For recursive majority the complexity is $\frac{n+1}{2}$. Finally, we provide an example of a function for which our lower bound is not tight. Our results imply new lower bound of $n - \mathsf{B}(n)$ on the multiplicative complexity of majority.Comment: Compared to the previous version we added a comparison of the complexity measures discussed to the degree of Boolean functions over $\mathbb{F}_2$. We removed the section on $MOD^3$ as a non-instructive example. We added the connection to multiplicative complexit

Exact Algorithms With Worst-case Guarantee For Scheduling: From Theory to Practice

This PhD thesis summarizes research works on the design of exact algorithms that provide a worst-case (time or space) guarantee for NP-hard scheduling problems. Both theoretical and practical aspects are considered with three main results reported. The first one is about a Dynamic Programming algorithm which solves the F3Cmax problem in O*(3^n) time and space. The algorithm is easily generalized to other flowshop problems and single machine scheduling problems. The second contribution is about a search tree method called Branch & Merge which solves the 1||SumTi problem with the time complexity converging to O*(2^n) and in polynomial space. Our third contribution aims to improve the practical efficiency of exact search tree algorithms solving scheduling problems. First we realized that a better way to implement the idea of Branch & Merge is to use a technique called Memorization. By the finding of a new algorithmic paradox and the implementation of a memory cleaning strategy, the method succeeded to solve instances with 300 more jobs with respect to the state-of-the-art algorithm for the 1||SumTi problem. Then the treatment is extended to another three problems 1|ri|SumCi, 1|dtilde|SumwiCi and F2||SumCi. The results of the four problems all together show the power of the Memorization paradigm when applied on sequencing problems. We name it Branch & Memorize to promote a systematic consideration of Memorization as an essential building block in branching algorithms like Branch and Bound. The method can surely also be used to solve other problems, which are not necessarily scheduling problems.Comment: 156 pages, PhD thesi