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Titlebook: Mathematical Optimization Theory and Operations Research: Recent Trends; 20th International C Alexander Strekalovsky,Yury Kochetov,Andrei O

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書目名稱Mathematical Optimization Theory and Operations Research: Recent Trends
副標(biāo)題20th International C
編輯Alexander Strekalovsky,Yury Kochetov,Andrei Orlov
視頻videohttp://file.papertrans.cn/627/626492/626492.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Mathematical Optimization Theory and Operations Research: Recent Trends; 20th International C Alexander Strekalovsky,Yury Kochetov,Andrei O
描述This book constitutes refereed proceedings of the 20th International Conference on?Mathematical Optimization Theory and Operations Research, MOTOR 2021, held in Irkutsk, Russia, in July 2021. Due to the COVID-19 pandemic the conference was held online.? ?.The 31 full papers and 3 short papers presented in this volume were carefully reviewed and selected from a total of 102 submissions. The papers in the volume are organised according to the following topical headings: continuous optimization; integer programming and combinatorial optimization; operational research applications; optimal control..
出版日期Conference proceedings 2021
關(guān)鍵詞optimization; artificial intelligence; computer systems; graph theory; theoretical computer science; comp
版次1
doihttps://doi.org/10.1007/978-3-030-86433-0
isbn_softcover978-3-030-86432-3
isbn_ebook978-3-030-86433-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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https://doi.org/10.1007/978-3-030-86433-0optimization; artificial intelligence; computer systems; graph theory; theoretical computer science; comp
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1865-0929 MOTOR 2021, held in Irkutsk, Russia, in July 2021. Due to the COVID-19 pandemic the conference was held online.? ?.The 31 full papers and 3 short papers presented in this volume were carefully reviewed and selected from a total of 102 submissions. The papers in the volume are organised according to
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Application of Smooth Approximation in Stochastic Optimization Problems with a Polyhedral Loss Funct and Heaviside function with sigmoid function. This replacement leads to the approximation of their gradients. Next, the problem solved using modified gradient descent. The accuracy and effectiveness of this method are shown in two examples. Also, we discuss some aspects of such approximations in the case of discrete random distribution.
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1865-0929 the following topical headings: continuous optimization; integer programming and combinatorial optimization; operational research applications; optimal control..978-3-030-86432-3978-3-030-86433-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
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