Do you want to publish a course? Click here

On sparse random combinatorial matrices

88   0   0.0 ( 0 )
 Added by Yury Person
 Publication date 2020
  fields
and research's language is English




Ask ChatGPT about the research

Let $Q_{n,d}$ denote the random combinatorial matrix whose rows are independent of one another and such that each row is sampled uniformly at random from the subset of vectors in ${0,1}^n$ having precisely $d$ entries equal to $1$. We present a short proof of the fact that $Pr[det(Q_{n,d})=0] = Oleft(frac{n^{1/2}log^{3/2} n}{d}right)=o(1)$, whenever $d=omega(n^{1/2}log^{3/2} n)$. In particular, our proof accommodates sparse random combinatorial matrices in the sense that $d = o(n)$ is allowed. We also consider the singularity of deterministic integer matrices $A$ randomly perturbed by a sparse combinatorial matrix. In particular, we prove that $Pr[det(A+Q_{n,d})=0]=Oleft(frac{n^{1/2}log^{3/2} n}{d}right)$, again, whenever $d=omega(n^{1/2}log^{3/2} n)$ and $A$ has the property that $(1,-d)$ is not an eigenpair of $A$.



rate research

Read More

Majority dynamics on a graph $G$ is a deterministic process such that every vertex updates its $pm 1$-assignment according to the majority assignment on its neighbor simultaneously at each step. Benjamini, Chan, ODonnel, Tamuz and Tan conjectured that, in the ErdH{o}s--Renyi random graph $G(n,p)$, the random initial $pm 1$-assignment converges to a $99%$-agreement with high probability whenever $p=omega(1/n)$. This conjecture was first confirmed for $pgeqlambda n^{-1/2}$ for a large constant $lambda$ by Fountoulakis, Kang and Makai. Although this result has been reproved recently by Tran and Vu and by Berkowitz and Devlin, it was unknown whether the conjecture holds for $p< lambda n^{-1/2}$. We break this $Omega(n^{-1/2})$-barrier by proving the conjecture for sparser random graphs $G(n,p)$, where $lambda n^{-3/5}log n leq p leq lambda n^{-1/2}$ with a large constant $lambda>0$.
We study a natural question about sparse random matrices which arises from an application in distributed computing: what is the distance from a fixed vector to the column span of a sparse random matrix $A in R^{n times m}$? We answer this question for several ensembles of sparse random matrices in which the average number of non-zero entries per column, $d$, is smaller than $log(n)$. Key to our analysis is a new characterization of linear dependencies in sparse random matrices. We show that with high probability, in certain random matrices, including rectangular matrices with i.i.d.~Bernoulli entries and $m geq (1 + epsilon)n$, and symmetric random matrices with Bernoulli entries, any linear dependency must be caused by one of three specific combinatorial structures. We show applications of our result to analyzing and designing em gradient codesem, replication schemes used in distributed machine learning to mitigate the effect of slow machines, called em stragglersem. We give the first known construction for a gradient code that achieves near-optimal error for both random and adversarial choices of stragglers.
We determine the rank of a random matrix over an arbitrary field with prescribed numbers of non-zero entries in each row and column. As an application we obtain a formula for the rate of low-density parity check codes. This formula vindicates a conjecture of Lelarge (2013). The proofs are based on coupling arguments and a novel random perturbation, applicable to any matrix, that diminishes the number of short linear relations.
189 - R. Glebov , M. Krivelevich 2012
We prove that the number of Hamilton cycles in the random graph G(n,p) is n!p^n(1+o(1))^n a.a.s., provided that pgeq (ln n+ln ln n+omega(1))/n. Furthermore, we prove the hitting-time version of this statement, showing that in the random graph process, the edge that creates a graph of minimum degree 2 creates (ln n/e)^n(1+o(1))^n Hamilton cycles a.a.s.
We consider the generalized game Lights Out played on a graph and investigate the following question: for a given positive integer $n$, what is the probability that a graph chosen uniformly at random from the set of graphs with $n$ vertices yields a universally solvable game of Lights Out? When $n leq 11$, we compute this probability exactly by determining if the game is universally solvable for each graph with $n$ vertices. We approximate this probability for each positive integer $n$ with $n leq 87$ by applying a Monte Carlo method using 1,000,000 trials. We also perform the analogous computations for connected graphs.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا