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» Sparse Recovery Using Sparse Random Matrices
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CORR
2011
Springer
167views Education» more  CORR 2011»
14 years 4 months ago
Fast global convergence of gradient methods for high-dimensional statistical recovery
Many statistical M-estimators are based on convex optimization problems formed by the weighted sum of a loss function with a norm-based regularizer. We analyze the convergence rat...
Alekh Agarwal, Sahand Negahban, Martin J. Wainwrig...
CORR
2008
Springer
178views Education» more  CORR 2008»
14 years 10 months ago
Model-Based Compressive Sensing
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals that can be well approximated by just K N elements from a...
Richard G. Baraniuk, Volkan Cevher, Marco F. Duart...
AAAI
1994
14 years 11 months ago
In Search of the Best Constraint Satisfaction Search
We present the results of an empirical study of several constraint satisfaction search algorithms and heuristics. Using a random problem generator that allows us to create instanc...
Daniel Frost, Rina Dechter
CDC
2009
IEEE
114views Control Systems» more  CDC 2009»
15 years 1 months ago
Parametric model order reduction accelerated by subspace recycling
Abstract-- Many model order reduction methods for parameterized systems need to construct a projection matrix V which requires computing several moment matrices of the parameterize...
Lihong Feng, Peter Benner, Jan G. Korvink
CVIU
2007
113views more  CVIU 2007»
14 years 9 months ago
Primal sketch: Integrating structure and texture
This article proposes a generative image model, which is called ‘‘primal sketch,’’ following Marr’s insight and terminology. This model combines two prominent classes of...
Cheng-en Guo, Song Chun Zhu, Ying Nian Wu