Simultaneous Perturbation Newton Algorithms for Simulation Optimization
Journal of Optimization Theory and Applications, ISSN: 1573-2878, Vol: 164, Issue: 2, Page: 621-643
2015
- 7Citations
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman–Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84890259497&origin=inward; http://dx.doi.org/10.1007/s10957-013-0507-1; http://link.springer.com/10.1007/s10957-013-0507-1; http://link.springer.com/content/pdf/10.1007/s10957-013-0507-1; http://link.springer.com/content/pdf/10.1007/s10957-013-0507-1.pdf; http://link.springer.com/article/10.1007/s10957-013-0507-1/fulltext.html; https://dx.doi.org/10.1007/s10957-013-0507-1; https://link.springer.com/article/10.1007/s10957-013-0507-1
Springer Science and Business Media LLC
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