Global minimizer of large scale stochastic rosenbrock function: canonical duality approach

Chaojie Li; David Yanf Gao

doi:10.1007/978-3-642-34478-7_82

Global minimizer of large scale stochastic rosenbrock function: canonical duality approach

Chaojie Li
, David Yanf Gao

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Canonical duality theory for solving the well-known benchmark test problem of stochastic Rosenbrock function is explored by two canonical transformations. Global optimality criterion is analytically obtained, which shows that the stochastic disturbance of these parameters could be eliminated by a proper canonical dual transformation. Numerical simulations illustrate the canonical duality theory is potentially powerful for solving this benchmark test problem and many other challenging problems in global optimization and complex network systems. © 2012 Springer-Verlag.

Original language	English
Title of host publication	Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
Pages	677-682
Volume	7666 LNCS
DOIs	https://doi.org/10.1007/978-3-642-34478-7_82
Publication status	Published - 2012
Externally published	Yes
Event	19th International Conference on Neural Information Processing (ICONIP 2012) - Doha, Qatar Duration: 12 Nov 2012 → 15 Nov 2012 https://link.springer.com/book/10.1007/978-3-642-34478-7

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7666 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Neural Information Processing (ICONIP 2012)
Place	Qatar
City	Doha
Period	12/11/12 → 15/11/12
Internet address	https://link.springer.com/book/10.1007/978-3-642-34478-7

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This research is supported by US Air Force Office of Scientific Research under the grant AFOSR FA9550-10-1-0487 and by Australia Government grant through the Collaborative Research Network(CRN) to the university of Ballarat.

Research Keywords

Canonical Duality Theory
Global Optimation
Stochastic Rosenbrock Function

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10.1007/978-3-642-34478-7_82

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Li, C., & Gao, D. Y. (2012). Global minimizer of large scale stochastic rosenbrock function: canonical duality approach. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings (Vol. 7666 LNCS, pp. 677-682). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7666 LNCS). https://doi.org/10.1007/978-3-642-34478-7_82

Li, Chaojie ; Gao, David Yanf. / Global minimizer of large scale stochastic rosenbrock function : canonical duality approach. Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. Vol. 7666 LNCS 2012. pp. 677-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Canonical duality theory for solving the well-known benchmark test problem of stochastic Rosenbrock function is explored by two canonical transformations. Global optimality criterion is analytically obtained, which shows that the stochastic disturbance of these parameters could be eliminated by a proper canonical dual transformation. Numerical simulations illustrate the canonical duality theory is potentially powerful for solving this benchmark test problem and many other challenging problems in global optimization and complex network systems. {\textcopyright} 2012 Springer-Verlag.",

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volume = "7666 LNCS",

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Li, C & Gao, DY 2012, Global minimizer of large scale stochastic rosenbrock function: canonical duality approach. in Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. vol. 7666 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7666 LNCS, pp. 677-682, 19th International Conference on Neural Information Processing (ICONIP 2012), Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34478-7_82

Global minimizer of large scale stochastic rosenbrock function: canonical duality approach. / Li, Chaojie; Gao, David Yanf.
Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. Vol. 7666 LNCS 2012. p. 677-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7666 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2012

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AB - Canonical duality theory for solving the well-known benchmark test problem of stochastic Rosenbrock function is explored by two canonical transformations. Global optimality criterion is analytically obtained, which shows that the stochastic disturbance of these parameters could be eliminated by a proper canonical dual transformation. Numerical simulations illustrate the canonical duality theory is potentially powerful for solving this benchmark test problem and many other challenging problems in global optimization and complex network systems. © 2012 Springer-Verlag.

KW - Canonical Duality Theory

KW - Global Optimation

KW - Stochastic Rosenbrock Function

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SN - 9783642344770

VL - 7666 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings

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Li C, Gao DY. Global minimizer of large scale stochastic rosenbrock function: canonical duality approach. In Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings. Vol. 7666 LNCS. 2012. p. 677-682. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-34478-7_82

Global minimizer of large scale stochastic rosenbrock function: canonical duality approach

Abstract

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Research Keywords

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