TY - GEN
T1 - Beyond local optimality
T2 - 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
AU - Gil, Stephanie
AU - Williams, Brian
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - Local convergence is a limitation of many optimization approaches for multimodal functions. For hybrid model learning, this can mean a compromise in accuracy. We develop an approach for learning the model parameters of hybrid discrete-continuous systems that avoids getting stuck in locally optimal solutions. We present an algorithm that implements this approach that 1) iteratively learns the locations and shapes of explored local maxima of the likelihood function, and 2) focuses the search away from these areas of the solution space, toward undiscovered maxima that are a priori likely to be optimal solutions. We evaluate the algorithm on Autonomous Underwater Vehicle (AUV) data. Our aggregate results show reduction in distance to the global maximum by 16% in 10 iterations, averaged over 100 trials, and iterative increase in log-liklihood value of learned model parameters, demonstrating the ability of the algorithm to guide the search toward increasingly better optima of the likelihood function, avoiding local convergence.
AB - Local convergence is a limitation of many optimization approaches for multimodal functions. For hybrid model learning, this can mean a compromise in accuracy. We develop an approach for learning the model parameters of hybrid discrete-continuous systems that avoids getting stuck in locally optimal solutions. We present an algorithm that implements this approach that 1) iteratively learns the locations and shapes of explored local maxima of the likelihood function, and 2) focuses the search away from these areas of the solution space, toward undiscovered maxima that are a priori likely to be optimal solutions. We evaluate the algorithm on Autonomous Underwater Vehicle (AUV) data. Our aggregate results show reduction in distance to the global maximum by 16% in 10 iterations, averaged over 100 trials, and iterative increase in log-liklihood value of learned model parameters, demonstrating the ability of the algorithm to guide the search toward increasingly better optima of the likelihood function, avoiding local convergence.
UR - http://www.scopus.com/inward/record.url?scp=77950823132&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77950823132&partnerID=8YFLogxK
U2 - 10.1109/CDC.2009.5400529
DO - 10.1109/CDC.2009.5400529
M3 - Conference contribution
AN - SCOPUS:77950823132
SN - 9781424438716
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3938
EP - 3945
BT - Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
Y2 - 15 December 2009 through 18 December 2009
ER -