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SIAMIS
2011
13 years 1 months ago
Gradient-Based Methods for Sparse Recovery
The convergence rate is analyzed for the sparse reconstruction by separable approximation (SpaRSA) algorithm for minimizing a sum f(x) + ψ(x), where f is smooth and ψ is convex, ...
William W. Hager, Dzung T. Phan, Hongchao Zhang
CORR
2010
Springer
128views Education» more  CORR 2010»
13 years 6 months ago
Sublinear Optimization for Machine Learning
Abstract--We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclos...
Kenneth L. Clarkson, Elad Hazan, David P. Woodruff
SODA
2010
ACM
261views Algorithms» more  SODA 2010»
14 years 4 months ago
Bidimensionality and Kernels
Bidimensionality theory appears to be a powerful framework in the development of meta-algorithmic techniques. It was introduced by Demaine et al. [J. ACM 2005 ] as a tool to obtai...
Fedor V. Fomin, Daniel Lokshtanov, Saket Saurabh, ...
PODS
2009
ACM
100views Database» more  PODS 2009»
14 years 7 months ago
Space-optimal heavy hitters with strong error bounds
The problem of finding heavy hitters and approximating the frequencies of items is at the heart of many problems in data stream analysis. It has been observed that several propose...
Radu Berinde, Graham Cormode, Piotr Indyk, Martin ...
JC
2008
128views more  JC 2008»
13 years 6 months ago
Lattice rule algorithms for multivariate approximation in the average case setting
We study multivariate approximation for continuous functions in the average case setting. The space of d variate continuous functions is equipped with the zero mean Gaussian measu...
Frances Y. Kuo, Ian H. Sloan, Henryk Wozniakowski