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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 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)
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)
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.(More)
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)
The stochastic root-finding problem (SRFP) is that of solving a nonlinear system of equations using only a simulation that provides estimates of the functions at requested points. Equivalently, SRFPs seek locations where an unknown vector function attains a given target using only a simulation capable of providing estimates of the function. SRFPs find(More)
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)
Consider the context of <i>constrained</i> Simulation Optimization (SO); that is, optimization problems where the objective and constraint functions are known through dependent Monte Carlo estimators. For solving such problems on <i>large finite spaces</i>, we provide an easily implemented sampling framework called SCORE (Sampling Criteria for Optimization(More)
The initial transient is an unavoidable issue when estimating parameters of steady-state distributions. We discuss contexts and factors that affect how the initial transient is handled, provide a bibliography (from the system simulation literature), discuss criteria for evaluating initial-transient algorithms, arguing for focusing on the mean squared error(More)
The Simulation-Optimization (SO) problem is a constrained optimization problem where the objective function is observed with error, usually through an oracle such as a simulation. Retrospective Approximation (RA) is a general technique that can be used to solve SO problems. In RA, the solution to the SO problem is approached using solutions to a sequence of(More)