Argonne-National-Laboratory / Dsp
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An open-source parallel optimization solver for structured mixed-integer programming
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DSP
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| Documentation |
DSP (Decomposition of Structured Programs) is an open-source and parallel package that implements decomposition methods for structured Mixed-Integer Quadratically Constrained Quadratic Programming (MIQCQP) problems. Structured programming problems refer to the class of problems that embed decomposable structures (e.g., block-angular matrices). Multiple decomposition methods can effectively utilize such structures in order to accelerate the solutions.
DSP Solution Methods:
- Extensive form solver (global solver)
- Serial/parallel dual decomposition (dual bounding solver)
- Serial/parallel Dantzig-Wolfe decomposition (global solver)
- Serial/parallel (integer) Benders decomposition
Problem Types:
- Two-stage stochastic MIQCQP problems
- Wasserstein-based distributionally robust variants
- Structured MIQCQPs
Problem Input Formats:
- SMPS file format for stochastic programs (possibly with optional arguments for Wasserstein distributionally robust)
- MPS and DEC files for generic block-structured optimization problems
- Julia modeling package DSPopt.jl
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.
- Geunyeong Byeon, Industrial Engineering, Arizona State University.
The contributors are listed in chronological order (first-come first-listed).
Key Publications
- Kibaek Kim. "Dual Decomposition of Two-Stage Distributionally Robust Mixed-Integer Programming under the Wasserstein Ambiguity Set" Optimization Online, 2020
- Kibaek Kim and Briand Dandurand. "Scalable Branching on Dual Decomposition of Stochastic Mixed-Integer Programming Problems" Mathematical Programming Computation (to appear), 2020
- Kibaek Kim, Cosmin Petra, and Victor Zavala. "An Asynchronous Bundle-Trust-Region Method for Dual Decomposition of Stochastic Mixed-Integer Programming" SIAM Journal on Optimization 29(1), 2019
- Kibaek Kim and Victor M. Zavala. "Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs" Mathematical Programming Computation 10(2), 2017
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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