Learning scenario representation for solving two-stage stochastic integer programs

• 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
• Institution: Singapore Management University

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...