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FOCS
2005
IEEE
16 years 19 hour ago
How to Pay, Come What May: Approximation Algorithms for Demand-Robust Covering Problems
Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among ...
Kedar Dhamdhere, Vineet Goyal, R. Ravi, Mohit Sing...
TSMC
2010
15 years 1 months ago
Distance Approximating Dimension Reduction of Riemannian Manifolds
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
Changyou Chen, Junping Zhang, Rudolf Fleischer
149
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JGO
2008
115views more  JGO 2008»
15 years 6 months ago
Smoothing by mollifiers. Part I: semi-infinite optimization
We show that a compact feasible set of a standard semi-infinite optimization problem can be approximated arbitrarily well by a level set of a single smooth function with certain r...
Hubertus Th. Jongen, Oliver Stein
ICDE
2006
IEEE
144views Database» more  ICDE 2006»
16 years 7 months ago
Approximately Processing Multi-granularity Aggregate Queries over Data Streams
Aggregate monitoring over data streams is attracting more and more attention in research community due to its broad potential applications. Existing methods suffer two problems, 1...
Shouke Qin, Weining Qian, Aoying Zhou
POPL
2010
ACM
16 years 3 months ago
A Theory of Indirection via Approximation
Building semantic models that account for various kinds of indirect reference has traditionally been a difficult problem. Indirect reference can appear in many guises, such as hea...
Aquinas Hobor, Robert Dockins, Andrew W. Appel