PySP swMATH ID: 4921 Software Authors: Watson, Jean-Paul; Woodruff, David L.; Hart, William E. Description: PySP: modeling and solving stochastic programs in Python. Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its wide-spread use. One factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of their deterministic counterparts, which are typically formulated first. A second factor relates to the difficulty of solving stochastic programming models, particularly in the mixed-integer, non-linear, and/or multi-stage cases. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times on large-scale problems. We simultaneously address both of these factors in our PySP software package, which is part of the Coopr open-source Python repository for optimization; the latter is distributed as part of IBM’s COIN-OR repository. To formulate a stochastic program in PySP, the user specifies both the deterministic base model (supporting linear, non-linear, and mixed-integer components) and the scenario tree model (defining the problem stages and the nature of uncertain parameters) in the Pyomo open-source algebraic modeling language. Given these two models, PySP provides two paths for solution of the corresponding stochastic program. The first alternative involves passing an extensive form to a standard deterministic solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets’ Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for obtaining approximate solutions to multi-stage stochastic programs. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve difficult stochastic programming problems. Homepage: https://pypi.python.org/pypi/coopr.pysp Related Software: Pyomo; CPLEX; Gurobi; DSP; AMPL; COOPR; ddsip; XPRESS; AIMMS; GitHub; SIPLIB; JuMP; Julia; Python; SCIP; StochasticPrograms.jl; mpi-sppy; Ipopt; SMPS reader; SUTIL Cited in: 28 Documents Standard Articles 2 Publications describing the Software, including 2 Publications in zbMATH Year PySP: modeling and solving stochastic programs in Python. Zbl 1275.90049Watson, Jean-Paul; Woodruff, David L.; Hart, William E. 2012 Pyomo – optimization modeling in Python. Zbl 1233.90002Hart, William E.; Laird, Carl; Watson, Jean-Paul; Woodruff, David L. 2012 all top 5 Cited by 67 Authors 10 Watson, Jean-Paul 8 Woodruff, David L. 3 Hackebeil, Gabriel A. 3 Ryan, Sarah M. 2 Bai, Ruibin 2 Grossmann, Ignacio E. 2 Hart, William E. 2 Jiang, Xiaoping 2 Kendall, Graham 2 Mildebrath, David T. 2 Weintraub, Andrés P. 2 Wets, Roger Jean-Baptiste 2 Zavala, Victor M. 1 Aravena, Ignacio 1 Barnett, Jason 1 Bernal, David E. 1 Biel, Martin 1 Çelik, Melih 1 Demir, Nur Banu 1 Dunning, Iain 1 Duran, Marco A. 1 Eckstein, Jonathan 1 Eskandani, Deniz 1 Fan, Jingnan 1 Feng, YongHan 1 Furman, Kevin C. 1 Gade, Dinakar 1 Garcia, Deanna 1 Greenberg, Harvey Joel 1 Gul, Serhat 1 Guo, Ge 1 Held, Harald 1 Hemmati, Mehdi 1 Huchette, Joey 1 Jalving, Jordan 1 Jansen, Peter W. 1 Johansson, Mikael 1 Kim, Kibaek 1 Knueven, Bernard 1 Laird, Carl D. 1 Landa-Silva, Dario 1 Li, Jiawei 1 Lubin, Miles 1 Maher, Stephen J. 1 Martinez, Gabriela E. 1 Martins, Joaquim R. R. A. 1 Muir, Christopher 1 Munguía, Lluís-Miquel 1 Munoz, Francisco D. 1 Ntaimo, Lewis 1 Oxberry, Geoffrey M. 1 Pais, Cristobal 1 Papavasiliou, Anthony 1 Perez, Ruben E. 1 Rajan, Deepak 1 Rathinam, Sivakumar 1 Ren, Jianfeng 1 Schaefer, Andrew J. 1 Shen, Zuo-Jun 1 Shin, Sungho 1 Shinano, Yuji 1 Siirola, John D. 1 Staid, Andrea 1 Stelzig, Philipp Emanuel 1 Valicka, Christopher G. 1 Veliz, Fernando Badilla 1 Wallace, Stein W. all top 5 Cited in 16 Serials 5 Mathematical Programming Computation 3 Operations Research Letters 3 European Journal of Operational Research 2 Computers & Operations Research 2 INFORMS Journal on Computing 2 Computational Management Science 1 Naval Research Logistics 1 Annals of Operations Research 1 Journal of Global Optimization 1 SIAM Review 1 Mathematical Programming. Series A. Series B 1 Computational Optimization and Applications 1 Optimization and Engineering 1 Structural and Multidisciplinary Optimization 1 Numerical Algebra, Control and Optimization 1 Springer Optimization and Its Applications all top 5 Cited in 6 Fields 28 Operations research, mathematical programming (90-XX) 2 Numerical analysis (65-XX) 2 Computer science (68-XX) 2 Game theory, economics, finance, and other social and behavioral sciences (91-XX) 1 General and overarching topics; collections (00-XX) 1 Calculus of variations and optimal control; optimization (49-XX) Citations by Year