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Convergence and cycling in Walker-type saddle search algorithms

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Levitt, Antoine and Ortner, Christoph (2017) Convergence and cycling in Walker-type saddle search algorithms. SIAM Journal on Numerical Analysis, 55 (5). pp. 2204-2227. doi:10.1137/16M1087199 ISSN 0036-1429.

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Official URL: https://doi.org/10.1137/16M1087199

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Abstract

Algorithms for computing local minima of smooth objective functions enjoy a mature theory as well as robust and efficient implementations. By comparison, the theory and practice of saddle search is destitute. In this paper we present results for idealized versions of the dimer and gentlest ascent (GAD) saddle search algorithms that show-case the limitations of what is theoretically achievable within the current class of saddle search algorithms: (1) we present an improved estimate on the region of attraction of saddles; (2) we give explicit examples of potential energy wells from which GAD-type dynamics are unable to escape; and (3) we present a local analysis of “singular points” around which the dynamics gets trapped, and prove the existence of quasi-periodic solutions. These results indicate that it is impossible to obtain globally convergent variants of dimer and GAD type algorithms.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): Algorithms, Maxima and minima
Journal or Publication Title: SIAM Journal on Numerical Analysis
Publisher: Society for Industrial and Applied Mathematics
ISSN: 0036-1429
Official Date: 12 September 2017
Dates:
DateEvent
12 September 2017Available
13 April 2017Accepted
Volume: 55
Number: 5
Page Range: pp. 2204-2227
DOI: 10.1137/16M1087199
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 19 April 2017
Date of first compliant Open Access: 1 May 2018
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
335120H2020 European Research Councilhttp://dx.doi.org/10.13039/100010663
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