Publications

These publications describe various Pyomo capabilitites or subpackages:

[Pyomo-paper]

William E. Hart, Jean-Paul Watson, David L. Woodruff. “Pyomo: modeling and solving mathematical programs in Python,” Mathematical Programming Computation, 3(3), August 2011.

[PyomoBookI]

William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff. Pyomo – Optimization Modeling in Python, Springer Optimization and Its Applications, Vol 67. Springer. 2012.

[PyomoBookII]

William E. Hart, Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson, John D. Siirola. Pyomo - Optimization Modeling in Python, 2nd Edition. Springer Optimization and Its Applications, Vol 67. Springer. 2017.

[PyomoBookIII]

Michael L. Bynum, Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, and David L. Woodruff. Pyomo - Optimization Modeling in Python, 3rd Edition. Vol. 67. Springer. 2021. DOI 10.1007/978-3-030-68928-5

[PyomoDAE-paper]

Bethany Nicholson, John D. Siirola, Jean-Paul Watson, Victor M. Zavala, and Lorenz T. Biegler. “pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations”, Mathematical Programming Computation, 10(2), 187-223. 2018.

[Parmest-paper]

Katherine A. Klise, Bethany L. Nicholson, Andrea Staid, David L.Woodruff. “Parmest: Parameter Estimation Via Pyomo.” Computer Aided Chemical Engineering, 47, 41-46. 2019.

[PyomoGDP-paper]

Qi Chen, Emma S. Johnson, David E. Bernal, Romeo Valentin, Sunjeev Kale, Johnny Bates, John D. Siirola, and Ignacio E. Grossmann. “Pyomo.GDP: an ecosystem for logic based modeling and optimization development.” Optimization and Engineering, 1-36. 2021. DOI 10.1007/s11081-021-09601-7

[PyomoGDP-proceedings]

Qi Chen, Emma S. Johnson, John D. Siirola, and Ignacio E. Grossmann. “Pyomo.GDP: Disjunctive Models in Python.” In M. R. Eden, M. G. Ierapetritou, and G. P. Towler (Eds.), Proceedings of the 13th International Symposium on Process Systems Engineering, 889–894, 2018. DOI 10.1016/B978-0-444-64241-7.50143-9

Bibliography

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[KMM+23]

B. Knueven, D. Mildebrath, C. Muir, J. D. Siirola, J.-P. Watson, and D. L. Woodruff. “A Parallel Hub-and-Spoke System for Large-Scale Scenario-Based Optimization Under Uncertainty”, Math Programming Computation, 15, 591-619. 2023. DOI 10.1007/s12532-023-00247-3

[KMT21]

J. Kronqvist, R. Misener, and C. Tsay. “Between Steps: Intermediate Relaxations between big-M and Convex Hull Reformulations”. 2021. https://arxiv.org/abs/2101.12708

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N. W. Sawaya and I. E. Grossmann. “A cutting plane method for solving linear generalized disjunctive programming problems”, Computer Aided Chemical Engineering, 15(C), 1032–1037. 2003. DOI 10.1016/S1570-7946(03)80444-3

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F. Trespalacios and I. E. Grossmann. “Improved Big-M reformulation for generalized disjunctive programs”, Computers and Chemical Engineering, 76, 98–103. 2015. DOI 10.1016/j.compchemeng.2015.02.013

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