[P6] S. Shin, V. Rao, M. Schanen, D. A. Maldonado, and M. Anitescu. Scalable multi-period AC optimal power flow utilizing GPUs with high memory capacities, 2024. [ arXiv ]
[P5] F. Pacaud, S. Shin, A. Montoison, M. Schanen, and M. Anitescu. Condensed-space methods for nonlinear programming on GPUs, 2024. [ arXiv ]
[P4] S. Shin and M. Anitescu. Improved perturbation bounds for graph-induced banded systems and application to optimal control. In Preparation.
[P3] S. Shin, S. Na, and M. Anitescu. Near-optimal performance of stochastic predictive control. Under Review. [ arXiv ]
[P2] F. Pacaud and S. Shin. GPU-accelerated nonlinear model predictive control with ExaModels and MadNLP, 2024. Under Review. [ arXiv ]
[P1] A. Engelmann, S. Shin, F. Pacaud, and V. M. Zavala. Scalable primal decomposition schemes for large-scale infrastructure networks, 2022. Under Review. [ arXiv ]

Journal Publications

[J13] F. Pacaud, M. Schanen, S. Shin, D. A. Maldonado, and M. Anitescu. Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architectures. Optimization Methods and Software, 2023. Accepted. [ arXiv ]
[J12] F. Pacaud, S. Shin, M. Schanen, D. A. Maldonado, and M. Anitescu. Accelerating condensed interior-point methods on SIMD/GPU architectures. Journal of Optimization Theory and Applications, pages 1--20, 2023. [ DOI | arXiv ]
[J11] S. Shin, Y. Lin, G. Qu, A. Wierman, and M. Anitescu. Near-optimal distributed linear-quadratic regulator for networked systems. SIAM Journal on Control and Optimization, 61(3):1113--1135, 2023. [ DOI | arXiv ]
[J10] S. Shin and V. M. Zavala. Diffusing-horizon model predictive control. IEEE Transactions on Automatic Control, 2023. [ DOI | arXiv ]
[J9] F. Pacaud, D. A. Maldonado, S. Shin, M. Schanen, and M. Anitescu. A feasible reduced space method for real-time optimal power flow. Electric Power Systems Research, 212:108268, 2022. [ DOI | arXiv ]
[J8] D. L. Cole, S. Shin, and V. Zavala. A julia framework for graph-structured nonlinear optimization. Industrial & Engineering Chemistry Research, 2022. [ DOI | arXiv ]
[J7] S. Na*, S. Shin*, M. Anitescu, and V. M. Zavala. On the convergence of overlapping schwarz decomposition for nonlinear optimal control. IEEE Transactions on Automatic Control, 2022. *Equal contribution. [ DOI | arXiv ]
[J6] J. Jalving, S. Shin, and V. M. Zavala. A graph-based modeling abstraction for optimization: Concepts and implementation in Plasmo.jl. Mathematical Programming Computation, 2022. [ DOI | arXiv ]
[J5] S. Shin, M. Anitescu, and V. M. Zavala. Exponential decay of sensitivity in graph-structured nonlinear programs. SIAM Journal on Optimization, 32(2):1156--1183, 2022. [ DOI | arXiv ]
[J4] S. Shin, V. M. Zavala, and M. Anitescu. Decentralized schemes with overlap for solving graph-structured optimization problems. IEEE Transactions on Control of Network Systems, 7(3):1225--1236, 2020. [ DOI | arXiv ]
[J3] S. Shin, P. Hart, T. Jahns, and V. M. Zavala. A hierarchical optimization architecture for large-scale power networks. IEEE Transactions on Control of Network Systems, 6(3):1004--1014, 2019. [ DOI | arXiv ]
[J2] S. Shin, O. S. Venturelli, and V. M. Zavala. Scalable nonlinear programming framework for parameter estimation in dynamic biological system models. PLoS Computational Biology, 15(3):e1006828, 2019. [ DOI ]
[J1] D. S. Kim, S. Shin, G. B. Choi, K. H. Jang, J. C. Suh, and J. M. Lee. Diagnosis of partial blockage in water pipeline using support vector machine with fault-characteristic peaks in frequency domain. Canadian Journal of Civil Engineering, 44(9):707--714, 2017. [ DOI ]

Conference Publications

[C9] S. Shin, F. Pacaud, and M. Anitescu. Accelerating optimal power flow with GPUs: SIMD abstraction of nonlinear programs and condensed-space interior-point methods. In XXIII Power Systems Computation Conference. Accepted. [ arXiv ]
[C8] S. Shin, F. Pacaud, E. Contantinescu, and M. Anitescu. Constrained policy optimization for stochastic optimal control under nonstationary uncertainties. In 2023 American Control Conference (ACC), 2023. [ arXiv ]
[C7] D. Cole, S. Shin, F. Pacaud, V. M. Zavala, and M. Anitescu. Exploiting GPU/SIMD architectures for solving linear-quadratic MPC problems. In 2023 American Control Conference (ACC), 2023. [ arXiv ]
[C6] S. Shin and V. M. Zavala. Controllability and observability imply exponential decay of sensitivity in dynamic optimization. In 7th IFAC Conference on Nonlinear Model Predictive Control, volume 54, pages 179--184, 2021. Young Author Award. [ DOI | arXiv | YouTube ]
[C5] S. Shin, C. Coffrin, K. Sundar, and V. M. Zavala. Graph-based modeling and decomposition of energy infrastructures. In 11th IFAC International Symposium on Advanced Control of Chemical Processes, volume 54, pages 693--698, 2021. Keynote Paper, Young Author Award. [ DOI | arXiv | YouTube ]
[C4] S. Shin, M. Anitescu, and V. M. Zavala. Overlapping Schwarz decomposition for constrained quadratic programs. In 2020 59th IEEE Conference on Decision and Control (CDC), pages 3004--3009, 2020. [ DOI | arXiv ]
[C3] Q. Lu, S. Shin, and V. M. Zavala. Characterizing the predictive accuracy of dynamic mode decomposition for data-driven control. In 21th IFAC World Congress, volume 53, pages 11289--11294, 2020. [ DOI | arXiv ]
[C2] S. Shin, T. Faulwasser, M. Zanon, and V. M. Zavala. A parallel decomposition scheme for solving long-horizon optimal control problems. In 2019 IEEE 58th Conference on Decision and Control (CDC), pages 5264--5271, 2019. [ DOI | arXiv ]
[C1] S. Shin, A. D. Smith, S. J. Qin, and V. M. Zavala. On the convergence of the dynamic inner PCA algorithm. In Foundations of Process Analytics and Machine Learning, 2019. [ arXiv ]