Learning scenario representation for solving two-stage stochastic integer programs

• المؤلفون الرئيسيون: WU, Yaoxin, SONG, Wen, CAO, Zhiguang, ZHANG, Jie
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2022
• الموضوعات: 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
• الوصول للمادة أونلاين: 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

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...