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126
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CDC
2008
IEEE
129views Control Systems» more  CDC 2008»
15 years 10 months ago
Distributed subgradient methods and quantization effects
Abstract— We consider a convex unconstrained optimization problem that arises in a network of agents whose goal is to cooperatively optimize the sum of the individual agent objec...
Angelia Nedic, Alexander Olshevsky, Asuman E. Ozda...
143
Voted
CORR
2011
Springer
204views Education» more  CORR 2011»
14 years 10 months ago
Accelerated Dual Descent for Network Optimization
—Dual descent methods are commonly used to solve network optimization problems because their implementation can be distributed through the network. However, their convergence rat...
Michael Zargham, A. Ribeiro, Ali Jadbabaie, Asuman...
ICPR
2000
IEEE
16 years 4 months ago
Image Recognition on the Neural Network Based on Multi-Valued Neurons
Multi-valued neurons are the neural processing elements with complex-valued weights, huge functionality (it is possible to implement on the single neuron arbitrary mapping describ...
Igor N. Aizenberg, Naum N. Aizenberg, Constantine ...
142
Voted
NIPS
2004
15 years 4 months ago
Convergence and No-Regret in Multiagent Learning
Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the environment is no longer stationary, t...
Michael H. Bowling
120
Voted
NIPS
1990
15 years 4 months ago
Convergence of a Neural Network Classifier
In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm ...
John S. Baras, Anthony LaVigna