This formulation is motivated from a document clustering problem in which one has a pairwise similarity function f learned from past data, and the goal is to partition the current set of documents in a way that correlates with f as much as possible; it can also be viewed as a kind of “agnostic learning” problem.Expand

This work considers the following problem: The Santa Claus has n presents that he wants to distribute among m kids, each kid has an arbitrary value for each present, and develops an O(log log m/log log log m) approximation algorithm for the restricted assignment case of the problem when p<sub>ij</sub>,0 (i.e. when present j has either value p <sub>j</sub> or 0 for each kid).Expand

The degree of unfairness under SRPT is surprisingly small, and closed-form expressions for mean response time as a function of job size are proved in this setting.Expand

The study of speed scaling to manage temperature is initiated and it is shown that the optimal temperature schedule can be computed offline in polynomial-time using the Ellipsoid algorithm and that no deterministic online algorithm can have a better competitive ratio.Expand

A constant-factor approximation is developed for a generalization of the orienteering problem in which both the start and the end nodes of the path are fixed, improving on the previously best known 4-approximation of [6].Expand

A theoretical framework is considered and a routing algorithm is proposed which exploits the patterns in the mobility of nodes to provide guarantees on the delay and the throughput achieved by the algorithm is only a poly-logarithmic factor off from the optimal.Expand

A method for improving the performance of web servers servicing static HTTP requests to give preference to requests for small files or requests with short remaining file size, in accordance with the SRPT (Shortest Remaining Processing Time) scheduling policy.Expand

A generalization of the stochastic online matching problem that also models preference-uncertainty and timeouts of buyers, and gives a constant factor approximation algorithm.Expand

The main idea in the algorithms is to produce a coloring over time by letting the color of the elements perform a random walk (with tiny increments) starting from 0 until they reach $\pm 1$.Expand

In this paper we introduce a new general framework for set covering problems, based on the combination of randomized rounding of the (near-)optimal solution of the linear programming (LP) relaxation,… Expand