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SIAMIS
2011
13 years 2 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
ICASSP
2011
IEEE
12 years 11 months ago
Distributed beamforming for multiuser peer-to-peer and multi-group multicasting relay networks
We generalize the concept of multiuser peer-to-peer (MUP2P) relay networks to that of a multi-group multicasting (MGM) relay network where each source may broadcast its message to...
Nils Bornhorst, Marius Pesavento, Alex B. Gershman
NIPS
2008
13 years 9 months ago
Fast Rates for Regularized Objectives
We study convergence properties of empirical minimization of a stochastic strongly convex objective, where the stochastic component is linear. We show that the value attained by t...
Karthik Sridharan, Shai Shalev-Shwartz, Nathan Sre...
NIPS
2007
13 years 9 months ago
Bundle Methods for Machine Learning
We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other ...
Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le
SIAMIS
2011
13 years 2 months ago
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
Abstract. Accurate signal recovery or image reconstruction from indirect and possibly undersampled data is a topic of considerable interest; for example, the literature in the rece...
Stephen Becker, Jérôme Bobin, Emmanue...