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» The Convergence of Iterated Classification
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CCE
2004
13 years 11 months ago
Improving convergence of the stochastic decomposition algorithm by using an efficient sampling technique
This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-st...
José María Ponce-Ortega, Vicente Ric...
NIPS
1998
14 years 10 days ago
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
Michael J. Kearns, Satinder P. Singh
GLOBECOM
2009
IEEE
14 years 2 months ago
Stochastic Resource Allocation over Fading Multiple Access and Broadcast Channels
In this paper, we consider the optimal rate and power allocation that maximizes a general utility function of average user rates in a fading multiple-access or broadcast channel. B...
Na Gao, Xin Wang
MOC
2000
105views more  MOC 2000»
13 years 10 months ago
On the iteratively regularized Gauss-Newton method for solving nonlinear ill-posed problems
The iteratively regularized Gauss-Newton method is applied to compute the stable solutions to nonlinear ill-posed problems F (x) = y when the data y is given approximately by y wit...
Jin Qi-nian
IPPS
2009
IEEE
14 years 5 months ago
Dynamic iterations for the solution of ordinary differential equations on multicore processors
In the past few years, there has been a trend of providing increased computing power through greater number of cores on a chip, rather than through higher clock speeds. In order t...
Yanan Yu, Ashok Srinivasan