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» Level-set methods for convex optimization
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WCE
2007
15 years 3 months ago
A Multidimensional Bisection Method for Minimizing Function over Simplex
—A new method for minimization problem over simplex, as a generalization of a well-known in onedimensional optimization bisection method is proposed. The convergence of the metho...
A. N. Baushev, E. Y. Morozova
CORR
2012
Springer
232views Education» more  CORR 2012»
13 years 9 months ago
Smoothing Proximal Gradient Method for General Structured Sparse Learning
We study the problem of learning high dimensional regression models regularized by a structured-sparsity-inducing penalty that encodes prior structural information on either input...
Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbone...
CORR
2008
Springer
133views Education» more  CORR 2008»
15 years 2 months ago
Estimating divergence functionals and the likelihood ratio by convex risk minimization
We develop and analyze M-estimation methods for divergence functionals and the likelihood ratios of two probability distributions. Our method is based on a non-asymptotic variatio...
XuanLong Nguyen, Martin J. Wainwright, Michael I. ...
COLT
2010
Springer
14 years 12 months ago
Composite Objective Mirror Descent
We present a new method for regularized convex optimization and analyze it under both online and stochastic optimization settings. In addition to unifying previously known firstor...
John Duchi, Shai Shalev-Shwartz, Yoram Singer, Amb...
SDM
2009
SIAM
152views Data Mining» more  SDM 2009»
15 years 11 months ago
Non-negative Matrix Factorization, Convexity and Isometry.
In this paper we explore avenues for improving the reliability of dimensionality reduction methods such as Non-Negative Matrix Factorization (NMF) as interpretive exploratory data...
Nikolaos Vasiloglou, Alexander G. Gray, David V. A...