A discrete parameter stochastic approximation algorithm for simulation optimization

Simulation

Volumn 81, Issue 11, 2005, Pages 757-772

  Bhatnagar, Shalabh ^a   Kowshik, Hemant J ^b  

^a INDIAN INSTITUTE OF SCIENCE   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

Admission control in communication networks; Discrete parameter optimization; Stochastic approximation algorithms; Two timescale SPSA

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; CONGESTION CONTROL (COMMUNICATION); CONVERGENCE OF NUMERICAL METHODS; OPTIMIZATION; PERTURBATION TECHNIQUES; QUEUEING NETWORKS; RANDOM PROCESSES; SENSITIVITY ANALYSIS; TELECOMMUNICATION NETWORKS;

ADMISSION CONTROL; CONVERGENCE ANALYSIS; DISCRETE PARAMETER OPTIMIZATION; SIMULTANEOUS PERTURBATION STOCHASTIC APPROXIMATION ALGORITHM;

ALGORITHMS;

EID: 31144448660     PISSN: 00375497     EISSN: 17413133     Source Type: Journal    
DOI: 10.1177/0037549705062294     Document Type: Article

References (28)

1. Gerencsér, L., S. D. Hill, and Z. Vágó. 1999. Optimization over discrete sets via SPSA. In Proceedings of the IEEE Conference on Decision and Control, pp. 1791-5.
2. Gokbayrak, K., and C. G. Cassandras. 2001. Online surrogate problem methodology for stochastic discrete resource allocation problems. Journal of Optimization Theory and Applications 108 (2): 349-75.
3. Barnhart, C. M., J. E. Wieselthier, and A. Ephremides. 1995. Admission-control policies for multihop wireless networks. Wireless Networks 1:373-87.
4. Kirkpatrick, S., C. D. Gelatt, and M. Vecchi. 1983. Optimization by simulated annealing. Science 220:621-80.
5. Gelfand, S. B., and S. K. Mitter. 1989. Simulated annealing with noisy or imprecise energy measurements. Journal of Optimization Theory and Applications 62 (1): 49-62.
6. Alrefaei, M. H., and S. Andradóttir. 1999. A simulated annealing algorithm with constant temperature for discrete stochastic optimization. Management Science 45 (5): 748-64.
7. Yan, D., and H. Mukai. 1992. Stochastic discrete optimization. SIAM Journal of Control and Optimization 30 (3): 594-612.
8. Gong, W.-B., Y.-C. Ho, and W. Zhai. 1999. Stochastic comparison algorithm for discrete optimization with estimation. SIAM Journal of Optimization 10 (2): 384-404.
9. Norkin, V. I., Y. M. Ermoliev, and A. Ruszczyński. 1998. On optimal allocation of indivisibles under uncertainty. Operations Research 46 (3): 381-95.
10. Jordan, S., and P. Varaiya. 1994. Control of multiple service multiple resource communication networks. IEEE Transactions on Communications 42 (11): 2979-88.
11. Cassandras, C. G., and C. G. Panayiotou. 1999. Concurrent sample path analysis of discrete event systems. Journal of Discrete Event Dynamic Systems: Theory and Applications 9:171-95.
12. Ho, Y.-C., and X.-R. Cao. 1991. Perturbation analysis of discrete event dynamical systems. Boston: Kluwer.
13. Gokbayrak, K., and C. G. Cassandras. 2002. Generalized surrogate problem methodology for online stochastic discrete optimization. Journal of Optimization Theory and Applications 114 (1): 97-132.
14. L'Ecuyer, P., and P. W. Glynn. 1994. Stochastic optimization by simulation: Convergence proofs for the GI/G/1 queue in steady-state. Management Science 40 (11): 1562-78.
15. Spall, J. C. 1992. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control 37 (3): 332-41.
16. Bhatnagar, S., and V. S. Borkar. 1998. A two time scale stochastic approximation scheme for simulation based parametric optimization. Probability in the Engineering and Informational Sciences 12:519-31.
17. Bhatnagar, S., M. C. Fu, S. I. Marcus, and S. Bhatnagar. 2001. Two timescale algorithms for simulation optimization of hidden Markov models. IIE Transactions 33 (3): 245-58.
18. Bhatnagar, S., and V. S. Borkar. 2003. Multiscale chaotic SPSA and smoothed functional algorithms for simulation optimization. SIMULATION 79 (10): 568-80.
19. Bhatnagar, S., M. C. Fu, S. I. Marcus, and I.-J. Wang. 2003. Two-timescale simultaneous perturbation stochastic approximation using deterministic perturbation sequences. ACM Transactions on Modelling and Computer Simulation 13 (2): 180-209.
20. Bhatnagar, S. 2005. Adaptive multivariate three-timescale stochastic approximation algorithms for simulation based optimization. ACM Transactions on Modeling and Computer Simulation 15 (1): 74-107.
21. Kelly, F. P., P. B. Key, and S. Zachary. 2000. Distributed admission control. IEEE Journal on Selected Areas in Communications 18:2617-28.
22. Liebeherr, J., D. E. Wrege, and D. Ferrari. 1996. Exact admission control for networks with a bounded delay service. IEEF/ACM Transactions on Networking 4 (6): 885-901.
23. Bertsekas, D. P., and J. N. Tsitsiklis. 1996. Neuro-dynamic programming. Belmont, MA: Athena Scientific.
24. Marbach, P., and J. N. Tsitsiklis. 2001. Simulation-based optimization of Markov reward processes. IEEE Transactions on Automatic Control 46 (2): 191-209.
25. Schweitzer, P. J. 1968. Perturbation theory and finite Markov chains. Journal of Applied Probability 5:401-13.
26. Vazquez-Abad, F. J., and H. J. Kushner. 1992. Estimation of the derivative of a stationary measure with respect to a control parameter. Journal of Applied Probability 29:343-52.
27. Hirsch, M. W. 1989. Convergent activation dynamics in continuous time networks. Neural Networks 2:331-49.
28. Bhatnagar, S., and I. B. B. Reddy. 2005. Optimal threshold policies for admission control in communication networks via discrete parameter stochastic approximation. Telecommunication Systems 29 (1): 9-31.