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102
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JAT
2006
64views more  JAT 2006»
15 years 16 days ago
Nonlinear function approximation: Computing smooth solutions with an adaptive greedy algorithm
Opposed to linear schemes, nonlinear function approximation allows to obtain a dimension independent rate of convergence. Unfortunately, in the presence of data noise typical algo...
Andreas Hofinger
SIGPRO
2011
209views Hardware» more  SIGPRO 2011»
14 years 7 months ago
Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms
In this paper, we survey and compare different algorithms that, given an overcomplete dictionary of elementary functions, solve the problem of simultaneous sparse signal approxim...
A. Rakotomamonjy
AAIM
2007
Springer
188views Algorithms» more  AAIM 2007»
15 years 6 months ago
Approximation Algorithms for the Graph Orientation Minimizing the Maximum Weighted Outdegree
Given an undirected graph G = (V, E) and a weight function w : E → Z+ , we consider the problem of orienting all edges in E so that the maximum weighted outdegree among all verti...
Yuichi Asahiro, Jesper Jansson, Eiji Miyano, Hirot...
IPL
1998
119views more  IPL 1998»
15 years 6 days ago
A 2.5-Factor Approximation Algorithm for the k-MST Problem
The k-MST problem requires finding that subset of at least k vertices of a given graph whose Minimum Spanning Tree has least weight amongst all subsets of at least k vertices. Th...
Sunil Arya, H. Ramesh
93
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TOMACS
2010
79views more  TOMACS 2010»
14 years 7 months ago
A stochastic approximation method with max-norm projections and its applications to the Q-learning algorithm
In this paper, we develop a stochastic approximation method to solve a monotone estimation problem and use this method to enhance the empirical performance of the Q-learning algor...
Sumit Kunnumkal, Huseyin Topaloglu