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DSP

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DSP is an open-source and parallel package that implements decomposition methods for structured mixed-integer programming problems. These are structured optimization problems in the following form:

    minimize   c^T x + \sum_{s=1}^S q_s^T y_s
    subject to   A x                              = b
               T_s x +                    W_s y_s = h_s for s = 1, .., S
               some x, y_s are integers

where x and y_s are decision variable vectors with dimensions n_1 and n_2, respectively, A, T_s and W_s are matrices of dimensions m_1 by n_1, m_2 by n_1 and m_2 by n_2, respectively, and c, q_s, b, and h_s are vectors of appropriate dimensions.

DSP Solution Methods:

  • Extensive form solver (global solver)
  • Serial/parallel dual decomposition (dual bounding solver)
  • Serial/parallel Dantzig-Wolfe decomposition (global solver)
  • Serial/parallel Benders decomposition

Problem Types:

  • Two-stage stochastic mixed-integer linear programs
  • Distributionally robust stochastic mixed-integer linear programs
  • Structured mixed-integer linear programs

Problem Input Formats:

  • SMPS file format for stochastic programs
  • MPS and DEC files for generic block-structured optimization problems
  • Julia modeling package DSP.jl

Documentation: Documentation Status

Installation

git clone --recursive https://github.com/Argonne-National-Laboratory/DSP.git

Contributors

  • Kibaek Kim, Mathematics and Computer Science Division, Argonne National Laboratory.
  • Victor M. Zavala, Department of Chemical and Biological Engineering, University of Wisconsin-Madison.
  • Christian Tjandraatmadja, Google Research.
  • Yingqiu Zhang, Industrial and Systems Engineering, Virginia Tech.

Key Publications

Acknowledgements

This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357.

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