Numerical continuation methods: an introduction
Numerical continuation methods: an introduction
Retrospective approximation algorithms for stochastic root finding
WSC '94 Proceedings of the 26th conference on Winter simulation
A projected stochastic approximation algorithm
WSC '91 Proceedings of the 23rd conference on Winter simulation
Stochastic optimization and the simultaneous perturbation method
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Elementary Numerical Analysis: An Algorithmic Approach
Elementary Numerical Analysis: An Algorithmic Approach
Retrospective approximation algorithms for the multidimensional stochastic root-finding problem
WSC '04 Proceedings of the 36th conference on Winter simulation
Finite-sample performance guarantees for one-dimensional stochastic root finding
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
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The stochastic root finding problem (SRFP) involves finding points in a region where a function attains a prespecified target value, using only a consistent estimator of the function. Due to the properties that the SRFP contexts entail, the development of good solutions to SRFPs has proven difficult, at least in the multi-dimensional setting. This paper discusses certain key issues, insights and complexities for SRFPs. Some of these are important in that they point to phenomena that contribute to the difficulties that arise in the development of efficient algorithms for SRFPs. Others are simply observations, sometimes obvious, but important for providing useful insight into algorithm development.