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- Vijay Arya, Naveen Garg, Rohit Khandekar, Adam Meyerson, Kamesh Munagala, Vinayaka Pandit
- SIAM J. Comput.
- 2001

In this paper, we analyze local search heuristics for the <italic>k</italic>-median and facility location problems. We define the {\em locality gap\/} of a local search procedure as the maximum ratio of a locally optimum solution (obtained using this procedure) to the global optimum. For <italic>k</italic>-median, we show that local search with swaps has a… (More)

- Sudipto Guha, Kamesh Munagala
- ArXiv
- 2007

The celebrated multi-armed bandit problem in decision theory models the central trade-off between exploration, or learning about the state of a system, and exploitation, or utilizing the system. In this paper we study the variant of the multi-armed bandit problem where the exploration phase involves costly experiments and occurs before the exploitation… (More)

- Kamesh Munagala, Abhiram G. Ranade
- SODA
- 1999

We show lower bounds of Q( $ Sort(V)) for the I/Ocomplexity of graph theoretic problems like connected components, biconnected components, and minimum spanning trees, where E and V are the number of edges and vertices in the input graph, respectively. We also present a deterministic O($ Sort(V) ’ max(l,loglog 9)) algorithm for the problem of graph… (More)

Web services are becoming a standard method of sharing data and functionality among loosely-coupled systems. We propose a general-purpose Web Service Management System (WSMS) that enables querying multiple web services in a transparent and integrated fashion. This paper tackles a first basic WSMS problem: query optimization for Select-Project-Join queries… (More)

- Shivnath Babu, Rajeev Motwani, Kamesh Munagala, Itaru Nishizawa, Jennifer Widom
- SIGMOD Conference
- 2004

We consider the problem of <i>pipelined filters</i>, where a continuous stream of tuples is processed by a set of commutative filters. Pipelined filters are common in stream applications and capture a large class of multiway stream joins. We focus on the problem of ordering the filters adaptively to minimize processing cost in an environment where stream… (More)

- Adam Meyerson, Kamesh Munagala, Serge A. Plotkin
- SIAM J. Comput.
- 2000

We present the COST-DISTANCE problem: finding a Steiner tree which optimizes the sum of edge costs along one metric and the sum of source-sink distances along an unrelated second metric. We give the first known randomized approximation scheme for COST-DISTANCE, where is the number of sources. We reduce several common network design problems to… (More)

- Gagan Goel, Sreenivas Gollapudi, Kamesh Munagala
- Theory of Computing
- 2010

In this paper, we present the first approximation algorithms for the problem of designing revenue optimal Bayesian incentive compatible auctions when there are multiple (heterogeneous) items and when bidders have arbitrary demand and budget constraints (and additive valuations). Our mechanisms are surprisingly simple: We show that a sequential all-pay… (More)

In this paper, we consider the problem of designing incentive compatible auctions for multiple (homogeneous) units of a good, when bidders have private valuations and private budget constraints. When only the valuations are private and the budgets are public, Dobzinski <i>et al</i> [8] show that the <i>adaptive clinching</i> auction is the unique… (More)

- Sudipto Guha, Adam Meyerson, Kamesh Munagala
- FOCS
- 2000

In this paper, we give the rst constant-approximations for a number of layered network design problems. We begin by modeling hierarchical caching, where caches are placed in layers and each layer satis es a xed percentage of the demand (bounded miss rates). We present a constant approximation to the minimum total cost of placing the caches and routing… (More)

- Kamesh Munagala, Utkarsh Srivastava, Jennifer Widom
- PODS
- 2007

We consider the problem of optimizing and executing multiple continuous queries, where each query is a conjunction of filters and each filter may occur in multiple queries. When filters are expensive, significant performance gains are achieved by sharing filter evaluations across queries. A shared execution strategy in our scenario can either be… (More)