Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
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2212.11571v2.pdf
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Accepted version
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987.12 KB
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Author(s)
Engelmann, Alexander
Shin, Sungho
Pacaud, François
Zavala, Victor M
Date Issued
June 2025
Journal
IEEE Transactions on Control of Network Systems
Publisher
Institute of Electrical and Electronics Engineers
Citation
A. Engelmann, S. Shin, F. Pacaud and V. M. Zavala, "Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks," in IEEE Transactions on Control of Network Systems, vol. 12, no. 2, pp. 1687-1698, June 2025.
Version
Author's final manuscript
Abstract
The operation of large-scale infrastructure networks requires scalable optimization schemes. To guarantee safe system operation, a high degree of feasibility in a small number of iterations is important. Decomposition schemes can help to achieve scalability. In terms of feasibility, however, classical approaches, such as the alternating direction method of multipliers (ADMMs), often converge slowly. In this work, we present primal decomposition schemes for hierarchically structured strongly convex quadratic programs. These schemes offer high degrees of feasibility in a small number of iterations in combination with global convergence guarantees. We benchmark their performance against the centralized off-the-shelf interior-point solver Ipopt and ADMM on problems with up to 300 000 decision variables and constraints. We find that the proposed approaches solve problems as fast as Ipopt, but with reduced communication and without requiring a full model exchange. Moreover, the proposed schemes achieve a higher accuracy than ADMM.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1109/tcns.2025.3526709
