arXiv subjects

Igor Shinkar

Publications and source records attributed to Igor Shinkar.

25 records · Page 2Linked to original sources

arXiv2015

On Percolation and $NP$-Hardness

We consider the robustness of computational hardness of problems whose input is obtained by applying independent random deletions to worst-case instances. For some classical $NP$-hard problems on graphs, such as Coloring, Vertex-Cover, and Hamiltonicity, we examine the complexity of these problems when edges (or vertices) of an arbitrary graph are deleted independently with probability $1-p > 0$. We prove that for $n$-vertex graphs, these problems remain as hard as in the worst-case, as long as $p > \frac{1}{n^{1-ε}}$ for arbitrary $ε\in (0,1)$, unless $NP \subseteq BPP$. We also prove hardness results for Constraint Satisfaction Problems, where random deletions are applied to clauses or variables, as well as the Subset-Sum problem, where items of a given instance are deleted at random.

cs.CC↗

arXiv2015

On Lipschitz Bijections between Boolean Functions

For two functions $f,g:\{0,1\}^n\to\{0,1\}$ a mapping $ψ:\{0,1\}^n\to\{0,1\}^n$ is said to be a $\textit{mapping from $f$ to $g$}$ if it is a bijection and $f(z)=g(ψ(z))$ for every $z\in\{0,1\}^n$. In this paper we study Lipschitz mappings between boolean functions. Our first result gives a construction of a $C$-Lipschitz mapping from the ${\sf Majority}$ function to the ${\sf Dictator}$ function for some universal constant $C$. On the other hand, there is no $n/2$-Lipschitz mapping in the other direction, namely from the ${\sf Dictator}$ function to the ${\sf Majority}$ function. This answers an open problem posed by Daniel Varga in the paper of Benjamini et al. (FOCS 2014). We also show a mapping from ${\sf Dictator}$ to ${\sf XOR}$ that is 3-local, 2-Lipschitz, and its inverse is $O(\log(n))$-Lipschitz, where by $L$-local mapping we mean that each of its output bits depends on at most $L$ input bits. Next, we consider the problem of finding functions such that any mapping between them must have large \emph{average stretch}, where the average stretch of a mapping $ϕ$ is defined as ${\sf avgStretch}(ϕ) = {\mathbb E}_{x,i}[dist(ϕ(x),ϕ(x+e_i)]$. We show that any mapping $ϕ$ from ${\sf XOR}$ to ${\sf Majority}$ must satisfy ${\sf avgStretch}(ϕ) \geq Ω(\sqrt{n})$. In some sense, this gives a "function analogue" to the question of Benjamini et al. (FOCS 2014), who asked whether there exists a set $A \subset \{0,1\}^n$ of density 0.5 such that any bijection from $\{0,1\}^{n-1}$ to $A$ has large average stretch. Finally, we show that for a random balanced function $f:\{0,1\}^n\to\{0,1\}^n$ with high probability there is a mapping $ϕ$ from ${\sf Dictator}$ to $f$ such that both $ϕ$ and $ϕ^{-1}$ have constant average stretch. In particular, this implies that one cannot obtain lower bounds on average stretch by taking uniformly random functions.

cs.DM↗

arXiv2014

Excited random walk with periodic cookies

In this paper we consider an excited random walk on $\mathbb{Z}$ in identically piled periodic environment. This is a discrete time process on $\mathbb{Z}$ defined by parameters $(p_1,\dots p_M) \in [0,1]^M$ for some positive integer $M$, where the walker upon the $i$-th visit to $z \in \mathbb{Z}$ moves to $z+1$ with probability $p_{i\pmod M}$, and moves to $z-1$ with probability $1-p_{i \pmod M}$. We give an explicit formula in terms of the parameters $(p_1,\dots,p_M)$ which determines whether the walk is recurrent, transient to the left, or transient to the right. In particular, in the case that $\frac{1}{M}\sum_{i=1}^{M}p_{i}=\frac {1}{2}$ all behaviors are possible, and may depend on the order of the $p_i$. Our framework allows us to reprove some known results on ERW with no additional effort.

math.PR↗

arXiv2014

Acquaintance Time of a Graph

We define the following parameter of connected graphs. For a given graph $G$ we place one agent in each vertex of $G$. Every pair of agents sharing a common edge is declared to be acquainted. In each round we choose some matching of $G$ (not necessarily a maximal matching), and for each edge in the matching the agents on this edge swap places. After the swap, again, every pair of agents sharing a common edge become acquainted, and the process continues. We define the \emph{acquaintance time} of a graph $G$, denoted by $AC(G)$, to be the minimal number of rounds required until every two agents are acquainted. We first study the acquaintance time for some natural families of graphs including the path, expanders, the binary tree, and the complete bipartite graph. We also show that for all positive integers $n$ and $k \leq n^{1.5}$ there exists an $n$-vertex graph $G$ such that $AC(G) =Θ(k)$. We also prove that for all $n$-vertex connected graphs $G$ we have $AC(G) = O\left(\frac{n^2}{\log(n)/\log\log(n)}\right)$, improving the $O(n^2)$ trivial upper bound achieved by sequentially letting each agent perform depth-first search along a spanning tree of $G$. Studying the computational complexity of this problem, we prove that for any constant $t \geq 1$ the problem of deciding that a given graph $G$ has $AC(G) \leq t$ or $AC(G) \geq 2t$ is $\mathcal{NP}$-complete. That is, $AC(G)$ is $\mathcal{NP}$-hard to approximate within multiplicative factor of 2, as well as within any additive constant factor. On the algorithmic side, we give a deterministic algorithm that given a graph $G$ with $AC(G)=1$ finds a ${\lceil n/c\rceil}$-rounds strategy for acquaintance in time $n^{c+O(1)}$. We also design a randomized polynomial time algorithm that given a graph $G$ with $AC(G)=1$ finds with high probability an $O(\log(n))$-rounds strategy for acquaintance.

cs.CC↗

arXiv2013

Bi-Lipschitz Bijection between the Boolean Cube and the Hamming Ball

We construct a bi-Lipschitz bijection from the Boolean cube to the Hamming ball of equal volume. More precisely, we show that for all even n there exists an explicit bijection f from the n-dimensional Boolean cube to the Hamming ball of equal volume embedded in (n+1)-dimensional Boolean cube, such that for all x and y it holds that distance(x,y) / 5 <= distance(f(x),f(y)) <= 4 distance(x,y) where distance(,) denotes the Hamming distance. In particular, this implies that the Hamming ball is bi-Lipschitz transitive. This result gives a strong negative answer to an open problem of Lovett and Viola [CC 2012], who raised the question in the context of sampling distributions in low-level complexity classes. The conceptual implication is that the problem of proving lower bounds in the context of sampling distributions will require some new ideas beyond the sensitivity-based structural results of Boppana [IPL 97]. We study the mapping f further and show that it (and its inverse) are computable in DLOGTIME-uniform TC0, but not in AC0. Moreover, we prove that f is "approximately local" in the sense that all but the last output bit of f are essentially determined by a single input bit.

math.CO↗

arXiv2013

A Tight Upper Bound on Acquaintance Time of Graphs

In this note we confirm a conjecture raised by Benjamini et al. \cite{BST} on the acquaintance time of graphs, proving that for all graphs $G$ with $n$ vertices it holds that $\AC(G) = O(n^{3/2})$, which is tight up to a multiplicative constant. This is done by proving that for all graphs $G$ with $n$ vertices and maximal degree $Δ$ it holds that $\AC(G) \leq 20 Δn$. Combining this with the bound $\AC(G) \leq O(n^2/Δ)$ from \cite{BST} gives the foregoing uniform upper bound of all $n$-vertex graphs. We also prove that for the $n$-vertex path $P_n$ it holds that $\AC(P_n)=n-2$. In addition we show that the barbell graph $B_n$ consisting of two cliques of sizes $\ceil{n/2}$ and $\floor{n/2}$ connected by a single edge also has $\AC(B_n) = n-2$. This shows that it is possible to add $Ω(n^2)$ edges to $P_n$ without changing the $\AC$ value of the graph.

math.CO↗

arXiv2012

Greedy Random Walk

We study a discrete time self interacting random process on graphs, which we call Greedy Random Walk. The walker is located initially at some vertex. As time evolves, each vertex maintains the set of adjacent edges touching it that have not been crossed yet by the walker. At each step, the walker being at some vertex, picks an adjacent edge among the edges that have not traversed thus far according to some (deterministic or randomized) rule. If all the adjacent edges have already been traversed, then an adjacent edge is chosen uniformly at random. After picking an edge the walk jumps along it to the neighboring vertex. We show that the expected edge cover time of the greedy random walk is linear in the number of edges for certain natural families of graphs. Examples of such graphs include the complete graph, even degree expanders of logarithmic girth, and the hypercube graph. We also show that GRW is transient in $\Z^d$ for all $d \geq 3$.

math.PR↗