Y.C. Ho, X. Cao. Perturbation analysis and optimization of queueing networks. Journal of Optimization Theory and Applications, 40(4), pp 559582. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Applications of Randomness in System Performance Measurement - Blackwell (1998) (2 citations) (Correct)
....Well designed circuits are not usually highly sensitive to parameter variations. That is, the figure of merit usually varies smoothly and monotonically, if not linearly, due to changes within the normal range of any one parameter, and usually not by as much as an order of magnitude. Ho and Cao [28] describe methods for approximate estimation of the sensitivity of queuing systems to small changes in the capacities of queues without running multiple simulations. The results are only accurate if events do not get reordered by the change. In TCP IP systems, queue overflows cause large changes ....
Y.C. Ho, X. Cao. Perturbation analysis and optimization of queueing networks. Journal of Optimization Theory and Applications, 40(4), pp 559582.
On Sampling-controlled Stochastic Approximation - Dupuis, Simha (1991) (3 citations) (Correct)
....in the definition of J(ff) typically ensures differentiability. Assumption A2 is a simple continuity assumption on the construction of more complicated estimators, g i , from the more fundamental estimators Y m . This assumption is easily verified for the gradient estimators of interest here [15, 30], as well those used in other recursions [31] and appears not to be a restrictive assumption. Assumption A3, which is a condition on the convergence behavior of the deterministic version, equation (5) reduces to a characterization of the function H. Consider what would happen if this condition ....
....the algorithm is gradient based and thus, the deterministic version uses analytic formulas for the gradient at each iterative step. Furthermore, we observe that several methods for direct gradient estimation in queueing systems have recently received a great deal of attention in the literature [15, 30] and therefore, a stochastic version of Gallager s algorithm, using these gradient estimates, is of general interest in the above application areas. We concern ourselves with applications in which direct gradient estimation is possible, including those which have already received some attention. ....
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Y.C.Ho and X.Cao, "Perturbation Analysis and Optimization of Queueing Networks", J. Optimization Theory & Applications, Vol. 40, No. 4, Aug 1983, pp. 559-582.
Central Limit Theorems For Stochastic Optimization.. - Tang, L'Ecuyer, Chen (1998) (Correct)
....of Systems Science, Academia Sinica, Beijing 100080, People s Republic of China. 1 Introduction Since the advent of perturbation analysis (PA) considerable effort has been devoted to studying PA based stochastic optimization algorithms for discrete event dynamic systems (DEDSs) Ho and Cao [15] proposed an off line Robbins Monro (RM) type stochastic optimization algorithm combined with a PA derivative estimator. On line or single run versions were promoted at an early stage in [11, 23, 31, 32, 33] among others. The RM type stochastic approximation (SA) algorithm combined with PA in a ....
Y. C. Ho and X. R. Cao, "Perturbation analysis and optimization of queueing networks," J. Optimiz. Theory Appl., vol.40, pp.559-582, 1983.
Applications of Distributed Artificial Intelligence in Industry - PARUNAK (1994) (19 citations) (Correct)
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Ho,Y.C. and Cao,X. (1983), "Perturbation Analysis and Optimization of queueing Networks." Journal of Optimization Theory and Applications. 40:4 (Aug. 1983), 559-82.
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