Optimization of computer simulation models with rare events

[1]  R. Rubinstein,et al.  Sensitivity Analysis of Insurance Risk Models Via Simulation , 1999 .

[2]  R. Rubinstein,et al.  Quick estimation of rare events in stochastic networks , 1997 .

[3]  Stephen M. Robinson,et al.  Sample-path optimization of convex stochastic performance functions , 1996, Math. Program..

[4]  A. Shapiro,et al.  Convergence analysis of gradient descent stochastic algorithms , 1996 .

[5]  Paul Glasserman,et al.  Rare-Event Simulation for Multistage Production-Inventory Systems , 1996 .

[6]  Yorai Wardi,et al.  Convergence Analysis of Stochastic Algorithms , 1996, Math. Oper. Res..

[7]  A. Shapiro.  Simulation-based optimization—convergence analysis and statistical inference , 1996 .

[8]  J. Dshalalow.  Advances in Queueing Theory, Methods, and Open Problems , 1995 .

[9]  Philip Heidelberger,et al.  Fast simulation of rare events in queueing and reliability models , 1993, TOMC.

[10]  Paul Glasserman,et al.  Analysis of an importance sampling estimator for tandem queues , 1995, TOMC.

[11]  P. Glynn,et al.  Stochastic optimization by simulation: numerical experiments with the M / M /1 queue in steady-state , 1994 .

[12]  Charles Leake,et al.  Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method , 1994 .

[13]  A. Shapiro,et al.  Nondifferentiability of the steady-state function in discrete event dynamic systems , 1994, IEEE Trans. Autom. Control..

[14]  P. Glynn,et al.  Stochastic Optimization by Simulation: Convergence Proofs for the GI/G/1 Queue in Steady-State , 1994 .

[15]  Alexander Shapiro,et al.  Asymptotic Behavior of Optimal Solutions in Stochastic Programming , 1993, Math. Oper. Res..

[16]  R. Rubinstein,et al.  Response surface estimation and sensitivity analysis via efficient change of measure , 1993 .

[17]  J. Sadowsky.  On the Optimality and Stability of Exponential Twisting in Monte Carlo Estimation , 1993, Proceedings. IEEE International Symposium on Information Theory.

[18]  Boris Polyak,et al.  Acceleration of stochastic approximation by averaging , 1992 .

[19]  R. Rubinstein,et al.  The efficiency and heavy traffic properties of the score function method in sensitivity analysis of queueing models , 1992, Advances in Applied Probability.

[20]  Georg Ch. Pflug.  Gradient estimates for the performance of markov chains and discrete event processes , 1992, Ann. Oper. Res..

[21]  Reuven Y. Rubinstein,et al.  Sensitivity analysis of discrete event systems by the “push out” method , 1992, Ann. Oper. Res..

[22]  Yuri M. Ermoliev,et al.  Stochastic quasigradient methods for optimization of discrete event systems , 1992, Ann. Oper. Res..

[23]  Philip Heidelberger,et al.  A Unified Framework for Simulating Markovian Models of Highly Dependable Systems , 1992, IEEE Trans. Computers.

[24]  Michael R. Frater,et al.  Optimally efficient estimation of the statistics of rare events in queueing networks , 1991 .

[25]  P. Ney,et al.  Monte Carlo simulation and large deviations theory for uniformly recurrent Markov chains , 1990, Journal of Applied Probability.

[26]  Jean Walrand,et al.  A quick simulation method for excessive backlogs in networks of queues , 1989 .

[27]  Sلأren Asmussen,et al.  Applied Probability and Queues , 1989 .

[28]  Upendra Dave,et al.  Applied Probability and Queues , 1987 .

[29]  Y. Ermoliev.  Stochastic quasigradient methods and their application to system optimization , 1983 .