Paper Info
Title
Accelerating the convergence of the stochastic ruler method for discrete stochastic optimization
Abstract
ABSTRACT We present two new variants of the stochastic ruler method for solving discrete stochastic optimization problems These two variants use the same mecha - nism for moving around the state space as the modi - ed stochastic ruler method we have proposed earlier However, the new variants use di erent approaches for estimating the optimal solution In particular, the modi ed stochastic ruler method uses the num - ber of visits to each state by the Markov chain gen - erated by the algorithm to estimate the optimal solu - tion On the other hand, one of our new methods uses the number of visits to each state by the embedded chain of the Markov chain generated by the algorithm to estimate the optimal solution, and our other new method uses the feasible solution with the best av - erage estimated objective function value to estimate the optimal solution Like our earlier modi cation of the stochastic ruler method, these two new meth - ods are guaranteed to converge almost surely to the set of global optimal solutions We present theoreti - cal and numerical results that indicate that our new approaches tend to lead to the set of global optimal solutions faster
Year
DOI
Venue
1997
10.1145/268437.268506
Proceedings of the 29th conference on Winter simulation
Keywords
Field
DocType
discrete stochastic optimization,stochastic ruler method,markov chain,global optimization,objective function,stochastic optimization,space technology,stochastic processes,modeling,state space,acceleration,convergence
Convergence (routing),Mathematical optimization,Stochastic optimization,Space technology,Computer science,Markov chain,Stochastic process,Almost surely,Ruler,State space
Conference
ISBN
Citations 
PageRank 
0-7803-4278-X
6
0.56
References 
Authors
7
2
Name
Order
Citations
PageRank
Mahmoud H. Alrefaei19010.14
Sigrún Andradóttir254855.09