We investigate variants of Lloyd's heuristic for clustering high-dimensional data in an attempt
to explain its popularity (a half century after its introduction) among practitioners, and in …

We give a general technique to obtain approximation mechanisms that are truthful in
expectation. We show that for packing domains, any α-approximation algorithm that also bounds …

We develop approximation algorithms for the problem of placing replicated data in arbitrary
networks, where the nodes may both issue requests for data objects and have capacity for …

We consider the Correlation Clustering problem introduced in [2]. Given a graph G=(V, E)
where each edge is labeled either “+”(similar) or “−”(different), we want to cluster the nodes so …

It is well known that in a network with arbitrary (convex) latency functions that are a function
of edge traffic, the worst-case ratio, over all inputs, of the system delay caused due to selfish …

We consider the Connected Facility Location problem. We are given a graph $G = (V,E)$
with costs $\{c_e\}$ on the edges, a set of facilities $\F \subseteq V$, and a set of clients $\D \…

Stochastic optimization problems provide a means to model uncertainty in the input data
where the uncertainty is modeled by a probability distribution over the possible realizations of …

We consider a fault-tolerant generalization of the classical uncapacitated facility location
problem, where each client j has a requirement that r j distinct facilities serve it, instead of just …

Stochastic optimization problems attempt to model uncertainty in the data by assuming that
the input is specified by a probability distribution. We consider the well-studied paradigm of 2-…

We consider the problem of makespan minimization on m unrelated machines in the context
of algorithmic mechanism design, where the machines are the strategic players. This is a …