Complexity of non-adaptive optimization algorithms for a class of diffusions

James M. Calvin; Peter W. Glynn

doi:10.1080/15326349608807389

Complexity of non-adaptive optimization algorithms for a class of diffusions

James M. Calvin, Peter W. Glynn

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

This paper is concerned with the analysis of the average error in approximating the global minimum of a 1-dimensional, time-homogeneous diffusion by non-adaptive methods. We derive the limiting distribution of the suitably normalized approximation error for both random and deterministic non-adaptive approximation methods. We identify the form of the asymptotically optimal random non-adaptive approximation methods.

Original language	English (US)
Pages (from-to)	343-365
Number of pages	23
Journal	Communications in Statistics. Part C: Stochastic Models
Volume	12
Issue number	3
DOIs	https://doi.org/10.1080/15326349608807389
State	Published - 1996
Externally published	Yes

All Science Journal Classification (ASJC) codes

Modeling and Simulation

Keywords

Average-case complexity
Diffusion processes
Global optimization

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10.1080/15326349608807389

Cite this

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title = "Complexity of non-adaptive optimization algorithms for a class of diffusions",

abstract = "This paper is concerned with the analysis of the average error in approximating the global minimum of a 1-dimensional, time-homogeneous diffusion by non-adaptive methods. We derive the limiting distribution of the suitably normalized approximation error for both random and deterministic non-adaptive approximation methods. We identify the form of the asymptotically optimal random non-adaptive approximation methods.",

keywords = "Average-case complexity, Diffusion processes, Global optimization",

author = "Calvin, \{James M.\} and Glynn, \{Peter W.\}",

note = "Funding Information: The first author was supported by the National Science Foundation under grant DDM-9010770. The second author was supported by the National Science Foundation under grant DDM-9101580 and the Army Research Office under Contract No. DAAL03-91-G-0319.",

year = "1996",

doi = "10.1080/15326349608807389",

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AU - Calvin, James M.

AU - Glynn, Peter W.

N1 - Funding Information: The first author was supported by the National Science Foundation under grant DDM-9010770. The second author was supported by the National Science Foundation under grant DDM-9101580 and the Army Research Office under Contract No. DAAL03-91-G-0319.

PY - 1996

Y1 - 1996

N2 - This paper is concerned with the analysis of the average error in approximating the global minimum of a 1-dimensional, time-homogeneous diffusion by non-adaptive methods. We derive the limiting distribution of the suitably normalized approximation error for both random and deterministic non-adaptive approximation methods. We identify the form of the asymptotically optimal random non-adaptive approximation methods.

AB - This paper is concerned with the analysis of the average error in approximating the global minimum of a 1-dimensional, time-homogeneous diffusion by non-adaptive methods. We derive the limiting distribution of the suitably normalized approximation error for both random and deterministic non-adaptive approximation methods. We identify the form of the asymptotically optimal random non-adaptive approximation methods.

KW - Average-case complexity

KW - Diffusion processes

KW - Global optimization

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JO - Communications in Statistics. Part C: Stochastic Models

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IS - 3

ER -

Complexity of non-adaptive optimization algorithms for a class of diffusions

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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