A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Asymptotic analysis of stochastic programs
Annals of Operations Research
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Acceleration of stochastic approximation by averaging
SIAM Journal on Control and Optimization
Sample-path optimization of convex stochastic performance functions
Mathematical Programming: Series A and B
Retrospective simulation response optimization
WSC '91 Proceedings of the 23rd conference on Winter simulation
Methods for Solving Systems of Nonlinear Equations
Methods for Solving Systems of Nonlinear Equations
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Computational Optimization and Applications
Global Stochastic Optimization with Low-Dispersion Point Sets
Operations Research
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Sample Approximation Approach for Optimization with Probabilistic Constraints
SIAM Journal on Optimization
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The mathematics of continuous-variable simulation optimization
Proceedings of the 40th Conference on Winter Simulation
Robust Stochastic Approximation Approach to Stochastic Programming
SIAM Journal on Optimization
Stochastic Root Finding and Efficient Estimation of Convex Risk Measures
Operations Research
An adaptive multidimensional version of the Kiefer-Wolfowitz stochastic approximation algorithm
Winter Simulation Conference
A Sequential Sampling Procedure for Stochastic Programming
Operations Research
Averaging and derivative estimation within stochastic approximation algorithms
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
An introspective on the retrospective-approximation paradigm
Proceedings of the Winter Simulation Conference
A Bayesian approach to stochastic root finding
Proceedings of the Winter Simulation Conference
Root finding via darts: dynamic adaptive random target shooting
Proceedings of the Winter Simulation Conference
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
INFORMS Journal on Computing
Computers and Operations Research
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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 simulation. After motivating SRFPs using a few examples, we review available methods to solve such problems on constrained Euclidean spaces. We present the current literature as three broad categories, and detail the basic theoretical results that are currently known in each of the categories. With a view towards helping the practitioner, we discuss specific variations in their implementable form, and provide references to computer code when easily available. Finally, we list a few questions that are worthwhile research pursuits from the standpoint of advancing our knowledge of the theoretical underpinnings and the implementation aspects of solutions to SRFPs.