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SIAMJO
2011
14 years 11 months ago
A Unifying Polyhedral Approximation Framework for Convex Optimization
Abstract. We propose a unifying framework for polyhedral approximation in convex optimization. It subsumes classical methods, such as cutting plane and simplicial decomposition, bu...
Dimitri P. Bertsekas, Huizhen Yu
HIS
2004
15 years 5 months ago
Stigmergy in Multi Agent Reinforcement Learning
In this paper, we describe how certain aspects of the biological phenomena of stigmergy can be imported into multiagent reinforcement learning (MARL), with the purpose of better e...
Raghav Aras, Alain Dutech, François Charpil...
CVPR
2007
IEEE
16 years 6 months ago
Utilizing Variational Optimization to Learn Markov Random Fields
Markov Random Field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an M...
Marshall F. Tappen
CEC
2005
IEEE
15 years 10 months ago
A population-based, steady-state procedure for real-parameter optimization
Despite the existence of a number of procedures for real-parameter optimization using evolutionary algorithms, there is still a need of a systematic and unbiased comparison of diļ...
Ankur Sinha, Santosh Tiwari, Kalyanmoy Deb
ATAL
2005
Springer
15 years 10 months ago
Impact of problem centralization in distributed constraint optimization algorithms
Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Probl...
John Davin, Pragnesh Jay Modi