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ARITH
2007
IEEE
15 years 3 months ago
Efficient polynomial L-approximations
We address the problem of computing a good floating-point-coefficient polynomial approximation to a function, with respect to the supremum norm. This is a key step in most process...
Nicolas Brisebarre, Sylvain Chevillard
FOCS
2006
IEEE
15 years 5 months ago
On the Optimality of the Dimensionality Reduction Method
We investigate the optimality of (1+ )-approximation algorithms obtained via the dimensionality reduction method. We show that: • Any data structure for the (1 + )-approximate n...
Alexandr Andoni, Piotr Indyk, Mihai Patrascu
CVPR
2000
IEEE
16 years 1 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
ICCAD
1995
IEEE
108views Hardware» more  ICCAD 1995»
15 years 3 months ago
Partitioning and reduction of RC interconnect networks based on scattering parameter macromodels
This paper presents a linear time algorithm to reduce a large RC interconnect network into subnetworks which are approximated with lower order equivalent RC circuits. The number o...
Haifang Liao, Wayne Wei-Ming Dai
SODA
2000
ACM
127views Algorithms» more  SODA 2000»
15 years 1 months ago
Dimensionality reduction techniques for proximity problems
In this paper we give approximation algorithms for several proximity problems in high dimensional spaces. In particular, we give the rst Las Vegas data structure for (1 + )-neares...
Piotr Indyk