TY - GEN
T1 - Limitation of Markov models and event-based learning and optimization
AU - Cao, Xi Ren
PY - 2008
Y1 - 2008
N2 - We first illustrate the possible limitations of the widely-used Markov model and then introduce the concepts of events, event-based policies and event-based optimization. Compared with the state-based policies, event-based policies may utilize the "future" information and therefore may perform better. In addition, the number of events may scale to the system size while the number of states grows exponentially. The event-based approach is particularly efficient for systems with special structural properties. The solutions to the event-based optimization can be developed with a sensitivity-based view, which is developed recently for the area of stochastic learning and optimization.
AB - We first illustrate the possible limitations of the widely-used Markov model and then introduce the concepts of events, event-based policies and event-based optimization. Compared with the state-based policies, event-based policies may utilize the "future" information and therefore may perform better. In addition, the number of events may scale to the system size while the number of states grows exponentially. The event-based approach is particularly efficient for systems with special structural properties. The solutions to the event-based optimization can be developed with a sensitivity-based view, which is developed recently for the area of stochastic learning and optimization.
UR - https://www.scopus.com/pages/publications/52349114487
U2 - 10.1109/CCDC.2008.4597263
DO - 10.1109/CCDC.2008.4597263
M3 - Conference Paper published in a book
AN - SCOPUS:52349114487
SN - 9781424417346
T3 - Chinese Control and Decision Conference, 2008, CCDC 2008
SP - 14
EP - 17
BT - Chinese Control and Decision Conference, 2008, CCDC 2008
T2 - Chinese Control and Decision Conference 2008, CCDC 2008
Y2 - 2 July 2008 through 4 July 2008
ER -