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Solving two-stage stochastic integer programs via representation learning

Neural Networks
Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph convolutional network (GCN) based VAE embeds scenarios into a low-dimensional latent space, conditioned ...
Yaoxin Wu   +3 more
openaire   +2 more sources

Stochastic decomposition for risk-averse two-stage stochastic linear programs

Journal of Global Optimization
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Prasad Parab   +2 more
openaire   +2 more sources

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