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Projection approach versus gradient descent for network’s flows assignment problem. / Krylatov, Alexander Yu; Shirokolobova, Anastasiya P.

Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers. Vol. 10556 LNCS Springer Nature, 2017. p. 345-350 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10556 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Krylatov, AY & Shirokolobova, AP 2017, Projection approach versus gradient descent for network’s flows assignment problem. in Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers. vol. 10556 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10556 LNCS, Springer Nature, pp. 345-350, 11th International Conference on Learning and Intelligent Optimization, LION 2017, Nizhny Novgorod, Russian Federation, 18/06/17. https://doi.org/10.1007/978-3-319-69404-7_29

APA

Krylatov, A. Y., & Shirokolobova, A. P. (2017). Projection approach versus gradient descent for network’s flows assignment problem. In Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers (Vol. 10556 LNCS, pp. 345-350). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10556 LNCS). Springer Nature. https://doi.org/10.1007/978-3-319-69404-7_29

Vancouver

Krylatov AY, Shirokolobova AP. Projection approach versus gradient descent for network’s flows assignment problem. In Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers. Vol. 10556 LNCS. Springer Nature. 2017. p. 345-350. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-69404-7_29

Author

Krylatov, Alexander Yu ; Shirokolobova, Anastasiya P. / Projection approach versus gradient descent for network’s flows assignment problem. Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers. Vol. 10556 LNCS Springer Nature, 2017. pp. 345-350 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{38bf1a4314f74ab29c125cf2d6c20a67,
title = "Projection approach versus gradient descent for network{\textquoteright}s flows assignment problem",
abstract = "The paper is devoted to comparison of two methodologically different types of mathematical techniques for coping with network{\textquoteright}s flows assignment problem. Gradient descent and projection approach are implemented to the simple network of parallel routes (there are no common arcs for any pair of routes). Gradient descent demonstrates zig-zagging behavior in some cases, while projection algorithm converge quadratically in the same conditions. Methodological interpretation of such phenomena is given.",
keywords = "Gradient descent, Network{\textquoteright}s flows assignment problem, Projection operator",
author = "Krylatov, {Alexander Yu} and Shirokolobova, {Anastasiya P.}",
year = "2017",
doi = "10.1007/978-3-319-69404-7_29",
language = "English",
isbn = "9783319694030",
volume = "10556 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "345--350",
booktitle = "Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers",
address = "Germany",
note = "11th International Conference on Learning and Intelligent Optimization, LION 2017 ; Conference date: 18-06-2017 Through 20-06-2017",

}

RIS

TY - GEN

T1 - Projection approach versus gradient descent for network’s flows assignment problem

AU - Krylatov, Alexander Yu

AU - Shirokolobova, Anastasiya P.

PY - 2017

Y1 - 2017

N2 - The paper is devoted to comparison of two methodologically different types of mathematical techniques for coping with network’s flows assignment problem. Gradient descent and projection approach are implemented to the simple network of parallel routes (there are no common arcs for any pair of routes). Gradient descent demonstrates zig-zagging behavior in some cases, while projection algorithm converge quadratically in the same conditions. Methodological interpretation of such phenomena is given.

AB - The paper is devoted to comparison of two methodologically different types of mathematical techniques for coping with network’s flows assignment problem. Gradient descent and projection approach are implemented to the simple network of parallel routes (there are no common arcs for any pair of routes). Gradient descent demonstrates zig-zagging behavior in some cases, while projection algorithm converge quadratically in the same conditions. Methodological interpretation of such phenomena is given.

KW - Gradient descent

KW - Network’s flows assignment problem

KW - Projection operator

UR - http://www.scopus.com/inward/record.url?scp=85034220216&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-69404-7_29

DO - 10.1007/978-3-319-69404-7_29

M3 - Conference contribution

AN - SCOPUS:85034220216

SN - 9783319694030

VL - 10556 LNCS

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

SP - 345

EP - 350

BT - Learning and Intelligent Optimization - 11th International Conference, LION 11, Revised Selected Papers

PB - Springer Nature

T2 - 11th International Conference on Learning and Intelligent Optimization, LION 2017

Y2 - 18 June 2017 through 20 June 2017

ER -

ID: 10309639