TY - JOUR
T1 - A framework for locally convergent random-search algorithms for discrete optimization via simulation
AU - Hong, L. Jeff
AU - Nelson, Barry L.
PY - 2007/9/1
Y1 - 2007/9/1
N2 - The goal of this article is to provide a general framework for locally convergent random-search algorithms for stochastic optimization problems when the objective function is embedded in a stochastic simulation and the decision variables are integer ordered. The framework guarantees desirable asymptotic properties, including almost-sure convergence and known rate of convergence, for any algorithms that conform to its mild conditions. Within this framework, algorithm designers can incorporate sophisticated search schemes and complicated statistical procedures to design new algorithms. © 2007 ACM.
AB - The goal of this article is to provide a general framework for locally convergent random-search algorithms for stochastic optimization problems when the objective function is embedded in a stochastic simulation and the decision variables are integer ordered. The framework guarantees desirable asymptotic properties, including almost-sure convergence and known rate of convergence, for any algorithms that conform to its mild conditions. Within this framework, algorithm designers can incorporate sophisticated search schemes and complicated statistical procedures to design new algorithms. © 2007 ACM.
KW - Discrete stochastic optimization
KW - Random search
UR - http://www.scopus.com/inward/record.url?scp=34648813290&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-34648813290&origin=recordpage
U2 - 10.1145/1276927.1276932
DO - 10.1145/1276927.1276932
M3 - RGC 21 - Publication in refereed journal
SN - 1049-3301
VL - 17
JO - ACM Transactions on Modeling and Computer Simulation
JF - ACM Transactions on Modeling and Computer Simulation
IS - 4
M1 - 1276932
ER -