@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",

}