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- Raghu Pasupathy, Sujin Kim
- ACM Trans. Model. Comput. Simul.
- 2011

The stochastic root-finding problem (SRFP) is that of finding the zero(s) of a vector function, that is, solving a nonlinear system of equations when the function is expressed implicitly through a stochastic simulation. SRFPs are equivalently expressed as stochastic fixed-point problems, where the underlying function is expressed implicitly via a noisy… (More)

- Raghu Pasupathy
- Operations Research
- 2010

The stochastic root-finding problem is that of finding a zero of a vector-valued function known only through a stochastic simulation. The simulation-optimization problem is that of locating a real-valued function’s minimum, again with only a stochastic simulation that generates function estimates. Retrospective approximation (RA) is a sample-path technique… (More)

We provide a review of the principle of sample-average approximation (SAA) for solving simulationoptimization problems. Our goal is to provide an accessible overview of the area and emphasize interesting recent work. We explain when one might want to use SAA and when one might expect it to provide good-quality solutions. We also review some of the key… (More)

- Honggang Wang, Raghu Pasupathy, Bruce W. Schmeiser
- ACM Trans. Model. Comput. Simul.
- 2013

We consider simulation-optimization (SO) models where the decision variables are integer ordered and the objective function is defined implicitly via a simulation oracle, which for any feasible solution can be called to compute a point estimate of the objective-function value. We develop R-SPLINE---a Retrospective-search algorithm that alternates between a… (More)

- Susan R. Hunter, Raghu Pasupathy
- INFORMS Journal on Computing
- 2013

Consider the context of selecting an optimal system from among a finite set of competing systems, based on a “stochastic” objective function and subject to multiple “stochastic” constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since… (More)

- Raghu Pasupathy, Shane G. Henderson
- Proceedings of the 2006 Winter Simulation…
- 2006

We propose a testbed of simulation-optimization problems. The purpose of the testbed is to encourage development and constructive comparison of simulation-optimization techniques and algorithms. We are particularly interested in increasing attention to the finite-time performance of algorithms, rather than the asymptotic results that one often finds in the… (More)

- Raghu Pasupathy, Shane G. Henderson
- Proceedings of the 2011 Winter Simulation…
- 2011

We present SimOpt --- a library of simulation-optimization problems intended to spur development and comparison of simulation-optimization methods and algorithms. The library currently has over 50 problems that are tagged by important problem attributes such as type of decision variables, and nature of constraints. Approximately half of the problems in the… (More)

We study control variate estimation where the control mean itself is estimated. Control variate estimation in simulation experiments can significantly increase sampling efficiency, and has traditionally been restricted to cases where the control has a known mean. In a previous paper (Schmeiser, Taaffe, and Wang 2000), we generalized the idea of control… (More)

- Susan R. Hunter, Raghu Pasupathy
- Proceedings of the 2010 Winter Simulation…
- 2010

We consider the problem of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to a single "stochastic" constraint. By strategically dividing the competing systems, we derive a large deviations sampling framework that asymptotically minimizes the probability of false selection. We… (More)

- Guy Feldman, Susan R. Hunter, Raghu Pasupathy
- 2015 Winter Simulation Conference (WSC)
- 2015

We consider the multi-objective simulation optimization problem on finite sets, where we seek the Pareto set corresponding to systems evaluated on multiple performance measures, using only Monte Carlo simulation observations from each system. We ask how a given simulation budget should be allocated across the systems, and a Pareto surface retrieved, so that… (More)