SIPLIB: A Stochastic Integer Programming Test Problem Library. SIPLIB is a collection of test problems to facilitate computational and algorithmic research in stochastic integer programming. The test problem data is provided in the standard SMPS format unless otherwise mentioned. Where available, information on the underlying problem formulation and known solution is also included.

References in zbMATH (referenced in 19 articles )

Showing results 1 to 19 of 19.
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  1. Aravena, Ignacio; Papavasiliou, Anthony: Asynchronous Lagrangian scenario decomposition (2021)
  2. Bansal, Manish; Zhang, Yingqiu: Scenario-based cuts for structured two-stage stochastic and distributionally robust (p)-order conic mixed integer programs (2021)
  3. Deng, Yan; Jia, Huiwen; Ahmed, Shabbir; Lee, Jon; Shen, Siqian: Scenario grouping and decomposition algorithms for chance-constrained programs (2021)
  4. Maher, Stephen J.: Implementing the branch-and-cut approach for a general purpose Benders’ decomposition framework (2021)
  5. Bakir, Ilke; Boland, Natashia; Dandurand, Brian; Erera, Alan: Sampling scenario set partition dual bounds for multistage stochastic programs (2020)
  6. Ryan, Kevin; Ahmed, Shabbir; Dey, Santanu S.; Rajan, Deepak; Musselman, Amelia; Watson, Jean-Paul: Optimization-driven scenario grouping (2020)
  7. Aldasoro, Unai; Merino, María; Pérez, Gloria: Time consistent expected mean-variance in multistage stochastic quadratic optimization: a model and a matheuristic (2019)
  8. Boland, Natashia; Christiansen, Jeffrey; Dandurand, Brian; Eberhard, Andrew; Oliveira, Fabricio: A parallelizable augmented Lagrangian method applied to large-scale non-convex-constrained optimization problems (2019)
  9. Munguía, Lluís-Miquel; Oxberry, Geoffrey; Rajan, Deepak; Shinano, Yuji: Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs (2019)
  10. Sarı Ay, Didem; Ryan, Sarah M.: Observational data-based quality assessment of scenario generation for stochastic programs (2019)
  11. Atakan, Semih; Sen, Suvrajeet: A progressive hedging based branch-and-bound algorithm for mixed-integer stochastic programs (2018)
  12. Bansal, Manish; Huang, Kuo-Ling; Mehrotra, Sanjay: Decomposition algorithms for two-stage distributionally robust mixed binary programs (2018)
  13. Deng, Yan; Ahmed, Shabbir; Shen, Siqian: Parallel scenario decomposition of risk-averse 0-1 stochastic programs (2018)
  14. Kim, Kibaek; Zavala, Victor M.: Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs (2018)
  15. Angulo, Gustavo; Ahmed, Shabbir; Dey, Santanu S.: Improving the integer L-shaped method (2016)
  16. Qiu, Feng; Ahmed, Shabbir; Dey, Santanu S.; Wolsey, Laurence A.: Covering linear programming with violations (2014)
  17. Colombo, Marco; Grothey, Andreas: A decomposition-based crash-start for stochastic programming (2013)
  18. Huang, Kai; Ahmed, Shabbir: The value of multistage stochastic programming in capacity planning under uncertainty (2009)
  19. Latorre, Jesús M.; Cerisola, Santiago; Ramos, Andrés; Palacios, Rafael: Analysis of stochastic problem decomposition algorithms in computational grids (2009)