An Introspective on the Retrospective-approximation Paradigm. Pasupathy, R. In Jain, S., Creasey, R. R., Himmelspach, J., White, K. P., & Fu, M., editors, Proceedings of the 2011 Winter Simulation Conference, pages 412–421, Piscataway, NJ, 2011. Institute of Electrical and Electronics Engineers, Inc.. ![An Introspective on the Retrospective-approximation Paradigm [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper abstract bibtex Retrospective Approximation (RA) is a solution paradigm introduced in the early 1990s by Chen and Schmeiser for solving one-dimensional stochastic root finding problems (SRFPs). The RA paradigm can be thought of as a refined and implementable version of sample average approximation, where a sequence of approximate problems are strategically generated and solved to identify iterates that progressively approach the desired solution. While originally aimed at one-dimensional SRFPs, the paradigm's broader utility, particularly within general simulation optimization algorithms, is becoming increasingly evident. We discuss the RA paradigm, demonstrate its usefulness, present the key results and papers on the topic over the last fifteen years, and speculate fruitful future directions.
@inproceedings{2011pasWSC,
author = {R. Pasupathy},
title = {An Introspective on the Retrospective-approximation Paradigm},
booktitle = {Proceedings of the 2011 Winter Simulation Conference},
Publisher = {Institute of Electrical and Electronics Engineers, Inc.},
Address = {Piscataway, NJ},
editor = {S. Jain and R. R. Creasey and J. Himmelspach and K. P. White and M. Fu},
pages = {412--421},
year = {2011},
url = {http://www.informs-sim.org/wsc11papers/035.pdf},
keywords = {retrospective approximation, stochastic root-finding},
abstract = {Retrospective Approximation (RA) is a solution paradigm introduced in the early 1990s by Chen and Schmeiser for solving one-dimensional stochastic root finding problems (SRFPs). The RA paradigm can be thought of as a refined and implementable version of sample average approximation, where a sequence of approximate problems are strategically generated and solved to identify iterates that progressively approach the desired solution. While originally aimed at one-dimensional SRFPs, the paradigm's broader utility, particularly within general simulation optimization algorithms, is becoming increasingly evident. We discuss the RA paradigm, demonstrate its usefulness, present the key results and papers on the topic over the last fifteen years, and speculate fruitful future directions.}}
%------ 2010 ------------------
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