arXiv subjects

Maxim Sviridenko

Publications and source records attributed to Maxim Sviridenko.

26 records · Page 2Linked to original sources

arXiv2014

Submodular Stochastic Probing on Matroids

In a stochastic probing problem we are given a universe $E$, where each element $e \in E$ is active independently with probability $p_e$, and only a probe of e can tell us whether it is active or not. On this universe we execute a process that one by one probes elements --- if a probed element is active, then we have to include it in the solution, which we gradually construct. Throughout the process we need to obey inner constraints on the set of elements taken into the solution, and outer constraints on the set of all probed elements. This abstract model was presented by Gupta and Nagarajan (IPCO '13), and provides a unified view of a number of problems. Thus far, all the results falling under this general framework pertain mainly to the case in which we are maximizing a linear objective function of the successfully probed elements. In this paper we generalize the stochastic probing problem by considering a monotone submodular objective function. We give a $(1 - 1/e)/(k_{in} + k_{out}+1)$-approximation algorithm for the case in which we are given $k_{in}$ matroids as inner constraints and $k_{out}$ matroids as outer constraints. Additionally, we obtain an improved $1/(k_{in} + k_{out})$-approximation algorithm for linear objective functions.

cs.DS↗

arXiv2013

Optimizing Maximum Flow Time and Maximum Throughput in Broadcast Scheduling

We consider the pull-based broadcast scheduling model. In this model, there are n unit-sized pages of information available at the server. Requests arrive over time at the server asking for a specific page. When the server transmits a page, all outstanding requests for the page are simultaneously satisfied, and this is what distinguishes broadcast scheduling from the standard scheduling setting where each job must be processed separately by the server. Broadcast scheduling has received a considerable amount of attention due to the algorithmic challenges that it gives in addition to its applications in multicast systems and wireless and LAN networks. In this paper, we give the following new approximation results for two popular objectives: - For the objective of minimizing the maximum flow time, we give the first PTAS. Previously, it was known that the algorithm First-In-First-Out (FIFO) is a 2-approximation, and it is tight. It has been suggested as an open problem to obtain a better approximation. - For the objective of maximizing the throughput, we give a 0.7759-approximation which improves upon the previous best known 0.75-approximation. Our improved results are enabled by our novel rounding schemes and linear programming which can effectively reduce congestion in schedule which is often the main bottleneck in designing scheduling algorithms based on linear programming. We believe that our algorithmic ideas and techniques could be of potential use for other scheduling problems.

cs.DS↗

arXiv2013

No-Wait Flowshop Scheduling is as Hard as Asymmetric Traveling Salesman Problem

In this paper we study the classical no-wait flowshop scheduling problem with makespan objective (F|no-wait|C_max in the standard three-field notation). This problem is well-known to be a special case of the asymmetric traveling salesman problem (ATSP) and as such has an approximation algorithm with logarithmic performance guarantee. In this work we show a reverse connection, we show that any polynomial time α-approximation algorithm for the no-wait flowshop scheduling problem with makespan objective implies the existence of a polynomial-time α(1+ε)-approximation algorithm for the ATSP, for any ε>0. This in turn implies that all non-approximability results for the ATSP (current or future) will carry over to its special case. In particular, it follows that no-wait flowshop problem is APX-hard, which is the first non-approximability result for this problem.

cs.DS↗

arXiv2013

Large Neighborhood Local Search for the Maximum Set Packing Problem

In this paper we consider the classical maximum set packing problem where set cardinality is upper bounded by $k$. We show how to design a variant of a polynomial-time local search algorithm with performance guarantee $(k+2)/3$. This local search algorithm is a special case of a more general procedure that allows to swap up to $Θ(\log n)$ elements per iteration. We also design problem instances with locality gap $k/3$ even for a wide class of exponential time local search procedures, which can swap up to $cn$ elements for a constant $c$. This shows that our analysis of this class of algorithms is almost tight.

cs.DS↗

arXiv2012

Concentration and Moment Inequalities for Polynomials of Independent Random Variables

In this work we design a general method for proving moment inequalities for polynomials of independent random variables. Our method works for a wide range of random variables including Gaussian, Boolean, exponential, Poisson and many others. We apply our method to derive general concentration inequalities for polynomials of independent random variables. We show that our method implies concentration inequalities for some previously open problems, e.g. permanent of a random symmetric matrices. We show that our concentration inequality is stronger than the well-known concentration inequality due to Kim and Vu. The main advantage of our method in comparison with the existing ones is a wide range of random variables we can handle and bounds for previously intractable regimes of high degree polynomials and small expectations. On the negative side we show that even for boolean random variables each term in our concentration inequality is tight.

math.PR↗

arXiv2012

Bernstein-like Concentration and Moment Inequalities for Polynomials of Independent Random Variables: Multilinear Case

We show that the probability that a multilinear polynomial $f$ of independent random variables exceeds its mean by $λ$ is at most $e^{-λ^2 / (R^q Var(f))}$ for sufficiently small $λ$, where $R$ is an absolute constant. This matches (up to constants in the exponent) what one would expect from the central limit theorem. Our methods handle a variety of types of random variables including Gaussian, Boolean, exponential, and Poisson. Previous work by Kim-Vu and Schudy-Sviridenko gave bounds of the same form that involved less natural parameters in place of the variance.

math.PR↗

arXiv2009

Non-monotone submodular maximization under matroid and knapsack constraints

Submodular function maximization is a central problem in combinatorial optimization, generalizing many important problems including Max Cut in directed/undirected graphs and in hypergraphs, certain constraint satisfaction problems, maximum entropy sampling, and maximum facility location problems. Unlike submodular minimization, submodular maximization is NP-hard. For the problem of maximizing a non-monotone submodular function, Feige, Mirrokni, and Vondrák recently developed a $2\over 5$-approximation algorithm \cite{FMV07}, however, their algorithms do not handle side constraints.} In this paper, we give the first constant-factor approximation algorithm for maximizing any non-negative submodular function subject to multiple matroid or knapsack constraints. We emphasize that our results are for {\em non-monotone} submodular functions. In particular, for any constant $k$, we present a $({1\over k+2+{1\over k}+ε})$-approximation for the submodular maximization problem under $k$ matroid constraints, and a $({1\over 5}-ε)$-approximation algorithm for this problem subject to $k$ knapsack constraints ($ε>0$ is any constant). We improve the approximation guarantee of our algorithm to ${1\over k+1+{1\over k-1}+ε}$ for $k\ge 2$ partition matroid constraints. This idea also gives a $({1\over k+ε})$-approximation for maximizing a {\em monotone} submodular function subject to $k\ge 2$ partition matroids, which improves over the previously best known guarantee of $\frac{1}{k+1}$.

cs.CC↗

arXiv2001

The diameter of a long range percolation graph

We consider the following long range percolation model: an undirected graph with the node set $\{0,1,...,N\}^d$, has edges $(\x,\y)$ selected with probability $\approx β/||\x-\y||^s$ if $||\x-\y||>1$, and with probability 1 if $||\x-\y||=1$, for some parameters $β,s>0$. This model was introduced by Benjamini and Berger, who obtained bounds on the diameter of this graph for the one-dimensional case $d=1$ and for various values of $s$, but left cases $s=1,2$ open. We show that, with high probability, the diameter of this graph is $Θ(\log N/\log\log N)$ when $s=d$, and, for some constants $0<η_1<η_2<1$, it is at most $N^{η_2}$, when $s=2d$ and is at least $N^{η_1}$ when $d=1,s=2,β<1$ or $s>2d$. We also provide a simple proof that the diameter is at most $\log^{O(1)}N$ with high probability, when $d<s<2d$, established previously by Berger and Benjamini.

math.PR↗