Learning scenario representation for solving two-stage stochastic integer programs

Learning scenario representation for solving two-stage stochastic integer programs

Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) based method to...

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Bibliographic Details
Main Authors: WU, Yaoxin, SONG, Wen, CAO, Zhiguang, ZHANG, Jie
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
Subjects:
Auto encoders
Combinatorial optimization problems
Convolutional networks
High complexity
Integer program
Learn+
Learning scenarios
Network-based
Stochastics
Uncertainty
Databases and Information Systems
OS and Networks
Online Access:https://ink.library.smu.edu.sg/sis_research/8163
https://ink.library.smu.edu.sg/context/sis_research/article/9166/viewcontent/LEARNING_SCENARIO_REPRESENTATION_FOR_SOLVING_TWO_STAGE_STOCHASTIC_INTEGER_PROGRAMS.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/8163
https://ink.library.smu.edu.sg/context/sis_research/article/9166/viewcontent/LEARNING_SCENARIO_REPRESENTATION_FOR_SOLVING_TWO_STAGE_STOCHASTIC_INTEGER_PROGRAMS.pdf

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