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Title: Matching stochastic algorithms to objective function landscapes
Related: Journal of global optimization, Vol. 31, Issue 4, (2005), p.579-598
DOI: 10.1007/s10898-004-9968-y
Publisher: Springer
Date: 2005
FoR/RFCD Code(s): 010200 Applied Mathematics 010300 Numerical and Computational Mathematics
Author/Creator: Baritompa, W. P
Author/Creator: Dür, M
Author/Creator: Hendrix, E. M. T
Author/Creator: Noakes, L
Author/Creator: Pullan, W. J
Author/Creator: Wood, G. R
Description: Large scale optimisation problems are frequently solved using stochastic methods. Such methods often generate points randomly in a search region in a neighbourhood of the current point, backtrack to get past barriers and employ a local optimiser. The aim of this paper is to explore how these algorithmic components should be used, given a particular objective function landscape. In a nutshell, we begin to provide rules for efficient travel, if we have some knowledge of the large or small scale geometry.
Description: 20 page(s)
Subject Keyword: 010200 Applied Mathematics
Subject Keyword: 010300 Numerical and Computational Mathematics
Subject Keyword: Backtracking
Subject Keyword: Global optimisation
Subject Keyword: Local optimisation
Subject Keyword: Search region
Subject Keyword: Simulated annealing
Subject Keyword: Temperature
Resource Type: journal article
Organisation: Macquarie University. Dept. of Statistics

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