Randomized Progressive Hedging methods for multi-stage stochastic programming
Author:
Funder
PGMO
Agence Nationale de la Recherche
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10479-020-03811-5.pdf
Reference24 articles.
1. Bauschke, H. H., & Combettes, P. L. (2011). Convex analysis and monotone operator theory in Hilbert spaces. Berlin: Springer.
2. Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59(1), 65–98. https://doi.org/10.1137/141000671.
3. Bianchi, P., Hachem, W., & Iutzeler, F. (2015). A coordinate descent primal–dual algorithm and application to distributed asynchronous optimization. IEEE Transactions on Automatic Control, 61(10), 2947–2957.
4. Biel, M., & Johansson, M. (2019). Efficient stochastic programming in Julia. arXiv preprint arXiv:1909.10451.
5. Combettes, P. L., & Pesquet, J. C. (2015). Stochastic quasi-Fejér block-coordinate fixed point iterations with random sweeping. SIAM Journal on Optimization, 25(2), 1221–1248.
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