| [P6] | Alexis Montoison, François Pacaud, Michael Saunders, Sungho Shin, and Dominique Orban.
MadNCL: a GPU implementation of algorithm NCL for large-scale, degenerate nonlinear programs.
2025.
arXiv:2510.05885. |
| [P5] | David Jin, Alexis Montoison, and Sungho Shin.
Harnessing Batched BLAS/LAPACK Kernels on GPUs for Parallel Solutions of Block Tridiagonal Systems.
2025.
arXiv:2509.03015. |
| [P4] | Alexis Montoison, François Pacaud, Sungho Shin, and Mihai Anitescu.
GPU implementation of second-order linear and nonlinear programming solvers.
2025.
arXiv:2508.16094. |
| [P3] | Runxin Ni, Sen Na, Sungho Shin, and Mihai Anitescu.
Distributed sequential quadratic programming with overlapping graph decomposition and exact augmented Lagrangian.
2024.
Under Review.
arXiv:2402.17170. |
| [P2] | François Pacaud, Sungho Shin, Alexis Montoison, Michel Schanen, and Mihai Anitescu.
Condensed-space methods for nonlinear programming on GPUs.
2024.
Under Review.
arXiv:2405.14236. |
| [P1] | Sungho Shin and Mihai Anitescu.
Improved approximation bounds for moore-penrose inverses of banded matrices with applications to continuous-time linear quadratic control.
2024.
Under Review.
arXiv:2411.04400. |
| [J16] | Sungho Shin, Sen Na, and Mihai Anitescu.
Near-optimal performance of stochastic model predictive control.
Mathematics of Operations Research, 2025.
Accepted.
arXiv:2210.08599. |
| [J15] | Alexander Engelmann, Sungho Shin, François Pacaud, and Victor M. Zavala.
Scalable primal decomposition schemes for large-scale infrastructure networks.
IEEE Transactions on Control of Network Systems, 2024.
Accepted.
arXiv:2212.11571. |
| [J14] | Sungho Shin, Mihai Anitescu, and François Pacaud.
Accelerating optimal power flow with GPUs: SIMD abstraction of nonlinear programs and condensed-space interior-point methods.
Electric Power Systems Research, 236:110651, 2024.
arXiv:2307.16830, doi:10.1016/j.epsr.2024.110651. |
| [J13] | François Pacaud, Michel Schanen, Sungho Shin, Daniel Adrian Maldonado, and Mihai Anitescu.
Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architectures.
Optimization Methods and Software, 39(4):874–897, 2024.
arXiv:2301.04869, doi:10.1080/10556788.2024.2329646. |
| [J12] | François Pacaud, Sungho Shin, Michel Schanen, Daniel Adrian Maldonado, and Mihai Anitescu.
Accelerating condensed interior-point methods on SIMD/GPU architectures.
Journal of Optimization Theory and Applications, pages 1–20, 2023.
arXiv:2203.11875, doi:10.1007/s10957-022-02129-5. |
| [J11] | Sungho Shin, Yiheng Lin, Guannan Qu, Adam Wierman, and Mihai Anitescu.
Near-optimal distributed linear-quadratic regulator for networked systems.
SIAM Journal on Control and Optimization, 61(3):1113–1135, 2023.
arXiv:2204.05551, doi:10.1137/22M1489836. |
| [J10] | Sungho Shin and Victor M Zavala.
Diffusing-horizon model predictive control.
IEEE Transactions on Automatic Control, 2023.
arXiv:2002.08556, doi:10.1109/TAC.2021.3137100. |
| [J9] | François Pacaud, Daniel Adrian Maldonado, Sungho Shin, Michel Schanen, and Mihai Anitescu.
A feasible reduced space method for real-time optimal power flow.
Electric Power Systems Research, 212:108268, 2022.
arXiv:2110.02590, doi:https://doi.org/10.1016/j.epsr.2022.108268. |
| [J8] | David L Cole, Sungho Shin, and Victor Zavala.
A julia framework for graph-structured nonlinear optimization.
Industrial & Engineering Chemistry Research, 2022.
arXiv:2204.05264, doi:https://doi.org/10.1021/acs.iecr.2c01253. |
| [J7] | Sen Na, Sungho Shin, Mihai Anitescu, and Victor M Zavala.
On the convergence of overlapping schwarz decomposition for nonlinear optimal control.
IEEE Transactions on Automatic Control, 2022.
arXiv:2005.06674, doi:10.1109/TAC.2022.3194087. |
| [J6] | Jordan Jalving, Sungho Shin, and Victor M Zavala.
A graph-based modeling abstraction for optimization: concepts and implementation in Plasmo.jl.
Mathematical Programming Computation, 2022.
arXiv:2006.05378, doi:10.1007/s12532-022-00223-3. |
| [J5] | Sungho Shin, Mihai Anitescu, and Victor M Zavala.
Exponential decay of sensitivity in graph-structured nonlinear programs.
SIAM Journal on Optimization, 32(2):1156–1183, 2022.
arXiv:2101.03067, doi:10.1137/21M1391079. |
| [J4] | Sungho Shin, Victor M Zavala, and Mihai Anitescu.
Decentralized schemes with overlap for solving graph-structured optimization problems.
IEEE Transactions on Control of Network Systems, 7(3):1225–1236, 2020.
arXiv:1810.00491, doi:10.1109/TCNS.2020.2967805. |
| [J3] | Sungho Shin, Philip Hart, Thomas Jahns, and Victor M Zavala.
A hierarchical optimization architecture for large-scale power networks.
IEEE Transactions on Control of Network Systems, 6(3):1004–1014, 2019.
arXiv:2002.09796, doi:10.1109/TCNS.2019.2906917. |
| [J2] | Sungho Shin, Ophelia S Venturelli, and Victor M Zavala.
Scalable nonlinear programming framework for parameter estimation in dynamic biological system models.
PLoS Computational Biology, 15(3):e1006828, 2019.
doi:10.1371/journal.pcbi.1006828. |
| [J1] | Dae Shik Kim, Sungho Shin, Go Bong Choi, Kwang Ho Jang, Jung Chul Suh, and Jong Min 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:10.1139/cjce-2016-0615. |
| [C10] | François Pacaud and Sungho Shin.
GPU-accelerated dynamic nonlinear optimization with ExaModels and MadNLP.
In 2024 IEEE 63rd Conference on Decision and Control (CDC), 5963–5968. 2024.
arXiv:2403.15913, doi:10.1109/CDC56724.2024.10886720. |
| [C9] | Sungho Shin, Vishwas Rao, Michel Schanen, D. Adrian Maldonado, and Mihai Anitescu.
Scalable multi-period AC optimal power flow utilizing GPUs with high memory capacities.
In Open Source Modelling and Simulation of Energy Systems. 2024.
Accepted.
arXiv:2405.14032. |
| [C8] | Sungho Shin, François Pacaud, Emil Contantinescu, and Mihai Anitescu.
Constrained policy optimization for stochastic optimal control under nonstationary uncertainties.
In 2023 American Control Conference (ACC). 2023.
arXiv:2209.13050. |
| [C7] | David Cole, Sungho Shin, François Pacaud, Victor M. Zavala, and Mihai Anitescu.
Exploiting GPU/SIMD architectures for solving linear-quadratic MPC problems.
In 2023 American Control Conference (ACC). 2023.
arXiv:2209.13049. |
| [C6] | Sungho Shin and Victor M Zavala.
Controllability and observability imply exponential decay of sensitivity in dynamic optimization.
In 7th IFAC Conference on Nonlinear Model Predictive Control, volume 54, 179–184. 2021.
Young Author Award.
arXiv:2101.06350, doi:10.1016/j.ifacol.2021.08.542. |
| [C5] | Sungho Shin, Carleton Coffrin, Kaarthik Sundar, and Victor M Zavala.
Graph-based modeling and decomposition of energy infrastructures.
In 11th IFAC International Symposium on Advanced Control of Chemical Processes, volume 54, 693–698. 2021.
Keynote Paper, Young Author Award.
arXiv:2010.02404, doi:10.1016/j.ifacol.2021.08.322. |
| [C4] | Sungho Shin, Mihai Anitescu, and Victor M Zavala.
Overlapping Schwarz decomposition for constrained quadratic programs.
In 2020 59th IEEE Conference on Decision and Control (CDC), volume, 3004–3009. 2020.
arXiv:2003.07502, doi:10.1109/CDC42340.2020.9304139. |
| [C3] | Qiugang Lu, Sungho Shin, and Victor M. Zavala.
Characterizing the predictive accuracy of dynamic mode decomposition for data-driven control.
In 21th IFAC World Congress, volume 53, 11289–11294. 2020.
arXiv:2003.01028, doi:https://doi.org/10.1016/j.ifacol.2020.12.373. |
| [C2] | Sungho Shin, Timm Faulwasser, Mario Zanon, and Victor M Zavala.
A parallel decomposition scheme for solving long-horizon optimal control problems.
In 2019 IEEE 58th Conference on Decision and Control (CDC), 5264–5271. 2019.
arXiv:1903.01055, doi:10.1109/CDC40024.2019.9030139. |
| [C1] | Sungho Shin, Alex D Smith, S Joe Qin, and Victor M Zavala.
On the convergence of the dynamic inner PCA algorithm.
In Foundations of Process Analytics and Machine Learning. 2019.
arXiv:2003.05928. |