J. Naor

Publications248
h-index55
Citations12,519
Highly Influential Citations1,208
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The Budgeted Maximum Coverage Problem
TLDR
This work proposes a (1−1/ e ) -approximation algorithm for the budgeted maximum coverage problem and argues that this approximation factor is the best possible, unless NP ⫅ DTIME (n O ( log log n) ) . Expand
Small-Bias Probability Spaces: Efficient Constructions and Applications
  • J. Naor, M. Naor
  • Mathematics, Computer Science
  • SIAM J. Comput.
  • 1 August 1993
It is shown how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with “almost” equal probability. They are… Expand
The Design of Competitive Online Algorithms via a Primal-Dual Approach
TLDR
This survey shows in this survey how to extend the primal—dual method to the setting of online algorithms, and shows its applicability to a wide variety of fundamental problems. Expand
A Tight Linear Time (1/2)-Approximation for Unconstrained Submodular Maximization
TLDR
This work presents a simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2, thus matching the known hardness result of Feige et al. Expand
Small-bias probability spaces: efficient constructions and applications
We show how to efficiently construct a small probability space on n binary random variables such that for every subset, its parity is either zero or one with "almost" equal probability. They are… Expand
Submodular Maximization with Cardinality Constraints
TLDR
Improved approximations for two variants of the cardinality constraint for non-monotone functions are presented and a simple randomized greedy approach is presented where in each step a random element is chosen from a set of "reasonably good" elements. Expand
Scheduling split intervals
TLDR
This work considers the problem of scheduling jobs that are given as groups of non-intersecting segments on the real line, and develops a 2-approximation algorithm for general instances, based on a novel version of the Local Ratio technique. Expand
The competitiveness of on-line assignments
TLDR
It is concluded that for the on-line problem where a number of servers are ready to provide service to a set of customers, randomized algorithms differ from deterministic ones by precisely a constant factor. Expand
A Tight Linear Time (1/2)-Approximation for Unconstrained Submodular Maximization
TLDR
A simple randomized linear time algorithm achieving a tight approximation guarantee of 1/2 is presented, thus matching the known hardness result of Feige, Mirrokni, and Vondrak. Expand
Near optimal placement of virtual network functions
TLDR
A thorough study of the NFV location problem is performed, it is shown that it introduces a new type of optimization problems, and near optimal approximation algorithms guaranteeing a placement with theoretically proven performance are provided. Expand
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