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JMLR
2006
124views more  JMLR 2006»
13 years 7 months ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
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
DATE
2008
IEEE
75views Hardware» more  DATE 2008»
14 years 2 months ago
Temperature Control of High-Performance Multi-core Platforms Using Convex Optimization
With technology advances, the number of cores integrated on a chip and their speed of operation is increasing. This, in turn is leading to a significant increase in chip temperat...
Srinivasan Murali, Almir Mutapcic, David Atienza, ...
CDC
2010
IEEE
176views Control Systems» more  CDC 2010»
13 years 2 months ago
Random projection algorithms for convex set intersection problems
The focus of this paper is on the set intersection problem for closed convex sets admitting projection operation in a closed form. The objective is to investigate algorithms that w...
Angelia Nedic
CDC
2008
IEEE
133views Control Systems» more  CDC 2008»
13 years 7 months ago
A nonlinear least squares estimation procedure without initial parameter guesses
Abstract-- This paper introduces a convex formulation approach for the initialization of parameter estimation problems (PEP). The proposed method exploits the parameter-affine feat...
Julian Bonilla Alarcon, Moritz Diehl, Bart De Moor...