Polynomial acceleration of Monte-Carlo global search

James M. Calvin

doi:10.1145/324138.324454

Polynomial acceleration of Monte-Carlo global search

James M. Calvin

Computer Science

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

Abstract

In this paper we describe a class of algorithms for approximating the global minimum of a function defined on a subset of d-dimensional Euclidean space. The algorithms are based on adaptively composing a number of simple Monte Carlo searches, and use a memory of a fixed finite number of observations. Within the class of algorithms it is possible to obtain arbitrary polynomial speedup in the asymptotic convergence rate compared with simple Monte Carlo.

Original language	English (US)
Pages (from-to)	673-677
Number of pages	5
Journal	Winter Simulation Conference Proceedings
Volume	1
DOIs	https://doi.org/10.1145/324138.324454
State	Published - 1999
Event	1999 Winter Simulation Conference Proceedings (WSC) - Phoenix, AZ, USA Duration: Dec 5 1999 → Dec 8 1999

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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10.1145/324138.324454

Cite this

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title = "Polynomial acceleration of Monte-Carlo global search",

abstract = "In this paper we describe a class of algorithms for approximating the global minimum of a function defined on a subset of d-dimensional Euclidean space. The algorithms are based on adaptively composing a number of simple Monte Carlo searches, and use a memory of a fixed finite number of observations. Within the class of algorithms it is possible to obtain arbitrary polynomial speedup in the asymptotic convergence rate compared with simple Monte Carlo.",

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AB - In this paper we describe a class of algorithms for approximating the global minimum of a function defined on a subset of d-dimensional Euclidean space. The algorithms are based on adaptively composing a number of simple Monte Carlo searches, and use a memory of a fixed finite number of observations. Within the class of algorithms it is possible to obtain arbitrary polynomial speedup in the asymptotic convergence rate compared with simple Monte Carlo.

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JF - Winter Simulation Conference Proceedings

T2 - 1999 Winter Simulation Conference Proceedings (WSC)

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ER -

Polynomial acceleration of Monte-Carlo global search

Abstract

All Science Journal Classification (ASJC) codes

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