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Primaldual approximation algorithms for metric facility location and kmedian problems
 Journal of the ACM
, 1999
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Gamps: Compressing multi sensor data by grouping and amplitude scaling
 In: ACM SIGMOD. (2009
"... We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within a given maximum (L∞) error ε. The problem arises naturally in applications that need to collect large amounts of data fr ..."
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Cited by 14 (0 self)
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We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within a given maximum (L∞) error ε. The problem arises naturally in applications that need to collect large amounts of data from multiple concurrent sources, such as sensors, servers and network routers, and archive them over a long period of time for offline data mining. We present GAMPS, a general framework that addresses this problem by combining several novel techniques. First, it dynamically groups multiple signals together so that signals within each group are correlated and can be maximally compressed jointly. Second, it appropriately scales the amplitudes of different signals within a group and compresses them within the maximum allowed reconstruction error bound. Our schemes are polynomial time O(α, β) approximation schemes, meaning that the maximum (L∞) error is at most αε and it uses at most β times the optimal memory. Finally, GAMPS maintains an index so that various queries can be issued directly on compressed data. Our experiments on several realworld sensor datasets show that GAMPS significantly reduces space without compromising the quality of search and query. Categories and Subject Descriptors
The General Steiner TreeStar Problem
 INFORMATION PROCESSING LETTERS
, 2002
"... The Steiner tree problem is de ned as follows  given a graph G = (V; E) and a subset X V of terminals, compute a minimum cost tree that includes all nodes in X . Furthermore, it is reasonable to assume that the edge costs form a metric. This problem is NPhard and has been the study of many heuri ..."
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Cited by 11 (0 self)
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The Steiner tree problem is de ned as follows  given a graph G = (V; E) and a subset X V of terminals, compute a minimum cost tree that includes all nodes in X . Furthermore, it is reasonable to assume that the edge costs form a metric. This problem is NPhard and has been the study of many heuristics and algorithms. We study a generalization of this problem, where there is a \switch" cost in addition to the cost of the edges. Switches are placed at internal nodes of the tree (essentially, we may assume that all nonleaf nodes of the Steiner tree have a switch). The cost for placing a switch may vary from node to node. A restricted version of this problem, where the terminal set X cannot be connected to each other directly but only via the Steiner nodes V n X , is referred to as the Steiner TreeStar problem. The General Steiner TreeStar problem does not require the terminal set and Steiner node set to be disjoint. This generalized problem can be reduced to the node weighted Steiner tree problem, for which algorithms with performance guarantees of (ln n) are known. However, such approach does not make use of the fact that the edge costs form a metric. In this paper we derive approximation algorithms with small constant factors for this problem. We show two dierent polynomial time algorithms with approximation factors of 5.16 and 5.
Constructing Treatment Portfolios Using Affinity Propagation
 In RECOMB
, 2008
"... Abstract. A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to ide ..."
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Cited by 8 (4 self)
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Abstract. A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint ’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design ’ and formalize it as a facility location problem. Finding a treatment portfolio is NPhard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recentlypublished affinity propagation algorithm work quite well for clustering tasks. We demonstrate this method using the two problems described above: selecting
Using linear programming in the design and analysis of approximation algorithms: two illustrative problems
 Approximation Algorithms for Combinatorial Optimization, K. Jansen and J. Rolim (Eds
, 1998
"... One of the foremost techniques in the design and analysis of approximation algorithms is to round the optimal solution to a linear programming relaxation in order to compute a nearoptimal solution to the problem at hand. We shall survey recent work in this vein for two particular problems: the un ..."
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Cited by 7 (0 self)
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One of the foremost techniques in the design and analysis of approximation algorithms is to round the optimal solution to a linear programming relaxation in order to compute a nearoptimal solution to the problem at hand. We shall survey recent work in this vein for two particular problems: the uncapacitated facility location problem and the problem of scheduling precedenceconstrained jobs on one machine so as to minimize a weighted average of their completion times.