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[J13]
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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 ]
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[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 ]
|
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[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 ]
|
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[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 ]
|
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[J8]
|
D. L. Cole, S. Shin, and V. Zavala.
A julia framework for graph-structured nonlinear optimization.
Industrial & Engineering Chemistry Research, 2022.
[ DOI |
arXiv ]
|
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[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 ]
|
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[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 ]
|
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[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 ]
|
|
[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 ]
|