Comparison of Monte Carlo and deterministic methods for non-adaptive optimization

Hisham A. Al-Mharmah; James M. Calvin

doi:10.1145/268437.268505

Comparison of Monte Carlo and deterministic methods for non-adaptive optimization

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper we compare the average performance of Monte Carlo methods for global optimization with non-adaptive deterministic alternatives. We analyze the behavior of the algorithms under the assumption of Wiener measure on the space of continuous functions on the unit interval. In this setting we show that the primary strength of the Monte Carlo methods (compositeness) is outweighed by the primary weakness (random gap size) when compared to efficient deterministic methods.

Original language	English (US)
Pages (from-to)	348-351
Number of pages	4
Journal	Winter Simulation Conference Proceedings
DOIs	https://doi.org/10.1145/268437.268505
State	Published - 1997
Externally published	Yes
Event	Proceedings of the 1997 Winter Simulation Conference - Atlanta, GA, USA Duration: Dec 7 1997 → Dec 10 1997

All Science Journal Classification (ASJC) codes

Software
Modeling and Simulation
Safety, Risk, Reliability and Quality
Chemical Health and Safety
Applied Mathematics

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10.1145/268437.268505

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title = "Comparison of Monte Carlo and deterministic methods for non-adaptive optimization",

abstract = "In this paper we compare the average performance of Monte Carlo methods for global optimization with non-adaptive deterministic alternatives. We analyze the behavior of the algorithms under the assumption of Wiener measure on the space of continuous functions on the unit interval. In this setting we show that the primary strength of the Monte Carlo methods (compositeness) is outweighed by the primary weakness (random gap size) when compared to efficient deterministic methods.",

author = "Al-Mharmah, {Hisham A.} and Calvin, {James M.}",

year = "1997",

doi = "10.1145/268437.268505",

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AB - In this paper we compare the average performance of Monte Carlo methods for global optimization with non-adaptive deterministic alternatives. We analyze the behavior of the algorithms under the assumption of Wiener measure on the space of continuous functions on the unit interval. In this setting we show that the primary strength of the Monte Carlo methods (compositeness) is outweighed by the primary weakness (random gap size) when compared to efficient deterministic methods.

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JO - Winter Simulation Conference Proceedings

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T2 - Proceedings of the 1997 Winter Simulation Conference

Y2 - 7 December 1997 through 10 December 1997

ER -

Comparison of Monte Carlo and deterministic methods for non-adaptive optimization

Abstract

All Science Journal Classification (ASJC) codes

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