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A Novel Parallel Adaptive Survivor Selection Framework for Large-Scale Simulation Optimization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Novel Parallel Adaptive Survivor Selection Framework for Large-Scale Simulation Optimization./
作者:	Pei, Linda.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	168 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Operations research. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29207680
ISBN:	9798438778226

A Novel Parallel Adaptive Survivor Selection Framework for Large-Scale Simulation Optimization.
Pei, Linda.

A Novel Parallel Adaptive Survivor Selection Framework for Large-Scale Simulation Optimization. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 168 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Northwestern University, 2022.

This item must not be sold to any third party vendors.

For stochastic simulation optimization in a modern computing era, we introduce a new parallel framework for solving very large-scale problems using a ranking & selection (R&S) approach that simulates all systems or feasible solutions to provide a global statistical guarantee. We propose a parallel adaptive survivor selection (PASS) framework that screens systems through sequential simulation and comparison to an adaptive estimated standard. This estimated standard is a surrogate for an unknown standard with a true value that is learned over time. Rather than adopt family-wise error statements and pairwise-comparisons approaches commonly found in traditional R&S procedures originally designed for a serial processor setting, PASS controls the expected false elimination rate and compares each system marginally to a single estimated standard comprised of aggregated data. In doing so, PASS avoids the curse of multiplicity and many of the computational bottlenecks that prevent other algorithms from scaling up. In a master-worker computing framework, we demonstrate the effectiveness of PASS on realistic problems with more than a million systems, and compare it to other parallel competitors. We develop a mathematical representation of PASS and establish results regarding its small sample and asymptotic behavior. We show that PASS is not only theoretically robust, but also practically efficacious: we analyze how its synchronous implementation ameliorates communication overhead prevalent in parallel environments and prove statistically that it can be combined with other procedures to provide a good selection guarantee.

ISBN: 9798438778226Subjects--Topical Terms:

547123
Operations research.
Subjects--Index Terms:

Large-scale

A Novel Parallel Adaptive Survivor Selection Framework for Large-Scale Simulation Optimization.
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300 $a 168 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Nelson, Barry.
502 $a Thesis (Ph.D.)--Northwestern University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a For stochastic simulation optimization in a modern computing era, we introduce a new parallel framework for solving very large-scale problems using a ranking & selection (R&S) approach that simulates all systems or feasible solutions to provide a global statistical guarantee. We propose a parallel adaptive survivor selection (PASS) framework that screens systems through sequential simulation and comparison to an adaptive estimated standard. This estimated standard is a surrogate for an unknown standard with a true value that is learned over time. Rather than adopt family-wise error statements and pairwise-comparisons approaches commonly found in traditional R&S procedures originally designed for a serial processor setting, PASS controls the expected false elimination rate and compares each system marginally to a single estimated standard comprised of aggregated data. In doing so, PASS avoids the curse of multiplicity and many of the computational bottlenecks that prevent other algorithms from scaling up. In a master-worker computing framework, we demonstrate the effectiveness of PASS on realistic problems with more than a million systems, and compare it to other parallel competitors. We develop a mathematical representation of PASS and establish results regarding its small sample and asymptotic behavior. We show that PASS is not only theoretically robust, but also practically efficacious: we analyze how its synchronous implementation ameliorates communication overhead prevalent in parallel environments and prove statistically that it can be combined with other procedures to provide a good selection guarantee.
590 $a School code: 0163.
650 4 $a Operations research. $3 547123
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653 $a Master-worker
653 $a Optimization
653 $a Parallel
653 $a Simulation
653 $a Stochastic
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793 $a English
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