Randomized algorithm for global optimization with bounded memory

James M. Calvin

doi:10.1016/j.matcom.2008.11.001

Randomized algorithm for global optimization with bounded memory

James M. Calvin

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

Abstract

We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of R^d. The algorithms are adaptive versions of Monte Carlo search and use a memory of a fixed number of past observations. By choosing a large enough memory, the convergence rate can be made to exceed any power of the convergence rate obtained with standard Monte Carlo search.

Original language	English (US)
Pages (from-to)	1068-1081
Number of pages	14
Journal	Mathematics and Computers in Simulation
Volume	80
Issue number	6
DOIs	https://doi.org/10.1016/j.matcom.2008.11.001
State	Published - Feb 2010
Externally published	Yes

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science
Numerical Analysis
Modeling and Simulation
Applied Mathematics

Keywords

Global optimization
Monte Carlo methods
Parallel algorithm
Point process
Randomized algorithm

Access to Document

10.1016/j.matcom.2008.11.001

Cite this

@article{20d1630bed654acb96d5810645f4eaee,

title = "Randomized algorithm for global optimization with bounded memory",

abstract = "We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of Rd. The algorithms are adaptive versions of Monte Carlo search and use a memory of a fixed number of past observations. By choosing a large enough memory, the convergence rate can be made to exceed any power of the convergence rate obtained with standard Monte Carlo search.",

keywords = "Global optimization, Monte Carlo methods, Parallel algorithm, Point process, Randomized algorithm",

author = "Calvin, \{James M.\}",

note = "Funding Information: This work was partially supported by grant CMMI-0825381 from the National Science Foundation of the United States.",

year = "2010",

month = feb,

doi = "10.1016/j.matcom.2008.11.001",

language = "English (US)",

volume = "80",

pages = "1068--1081",

journal = "Mathematics and Computers in Simulation",

issn = "0378-4754",

publisher = "Elsevier B.V.",

number = "6",

}

TY - JOUR

T1 - Randomized algorithm for global optimization with bounded memory

AU - Calvin, James M.

N1 - Funding Information: This work was partially supported by grant CMMI-0825381 from the National Science Foundation of the United States.

PY - 2010/2

Y1 - 2010/2

N2 - We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of Rd. The algorithms are adaptive versions of Monte Carlo search and use a memory of a fixed number of past observations. By choosing a large enough memory, the convergence rate can be made to exceed any power of the convergence rate obtained with standard Monte Carlo search.

AB - We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of Rd. The algorithms are adaptive versions of Monte Carlo search and use a memory of a fixed number of past observations. By choosing a large enough memory, the convergence rate can be made to exceed any power of the convergence rate obtained with standard Monte Carlo search.

KW - Global optimization

KW - Monte Carlo methods

KW - Parallel algorithm

KW - Point process

KW - Randomized algorithm

UR - https://www.scopus.com/pages/publications/77649271570

UR - https://www.scopus.com/pages/publications/77649271570#tab=citedBy

U2 - 10.1016/j.matcom.2008.11.001

DO - 10.1016/j.matcom.2008.11.001

M3 - Article

AN - SCOPUS:77649271570

SN - 0378-4754

VL - 80

SP - 1068

EP - 1081

JO - Mathematics and Computers in Simulation

JF - Mathematics and Computers in Simulation

IS - 6

ER -

Randomized algorithm for global optimization with bounded memory

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this