Publications in 2021

[1] Michael L. Stein. “A weighted composite log-likelihood approach to parametric estimation of the extreme quantiles of a distribution”. Submitted (2021).

[2] Joshua L Pulsipher, Weiqi Zhang, Tyler J Hongisto, and Victor M Zavala. “A Unifying Modeling Abstraction for Infinite-Dimensional Optimization”. In: Computers & Chemical Engineering (In Press 2021).

[3] Joshua L Pulsipher, Benjamin R Davidson, and Victor M Zavala. “New Measures for Shaping Trajectories in Dynamic Optimization”. In: arXiv preprint arXiv (2021).

[4] Jacob Price, Brek Meuris, Madelyn Shapiro, and Panos Stinis. “Optimal renormalization of multiscale systems”. In: Proceedings of the National Academy of Sciences 118.37 (2021). issn: 0027-8424.

[5] M. Krock, J. Bessac, M. L. Stein, and A. H. Monahan. “Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails”. To be submitted (2021).

[6] A. Lenzi, J. Bessac, and M. Anitescu. “Power Grid Frequency Prediction Using Spatio-Temporal Modeling”. In: Journal of Statistical Analysis and Data Mining: The ASA Data Science Journal (2021), pp. 1-14.

[7] Sheng Zhang, Xiu Yang, Samy Tindel, and Guang Lin. “Augmented Gaussian random field: Theory and computation”. In: Discrete & Continuous Dynamical Systems – S 0 (2021).

[8] Xiu Yang Mengqi Hu Yifei Lou. “A general framework of rotational sparse approximation in uncertainty quantification”. In: arXiv Preprint (2021).

[9] Xiu Yang Didem Kochan Zheng Zhang. “A Quantum-Inspired Hamiltonian Monte Carlo Method for Missing Data Imputation”. In: submitted to SIAM International Conference on Data Mining (2021).

[10] A. Lenzi, J. Bessac, and M. Anitescu. “Predicting disturbances in power grid systems with spatio-temporal modelling and Bayesian decision theory – Peer-reviewed journal submission”. (2021).

[11] A. Lenzi, J. Bessac, J. Rudi, and M. Stein. “Neural networks for parameter estimation of intractable likelihoods”. Peer-reviewed journal submission (2021).

[12] Greg Ongie, Daniel Pimentel-Alarc on, Laura Balzano, Rebecca Willett, and Robert D
Nowak. “Tensor Methods for Nonlinear Matrix Completion”. In: SIAM Journal on Mathematics of Data Science 3.1 (2021), pp. 253-279.

[13] Hyebin Song, Garvesh Raskutti, and Rebecca Willett. “Prediction in the presence of response-dependent missing labels”. In: arXiv preprint arXiv:2103.13555 (2021).

[14] X. Wu, L. Wang, I Cristali, Q. Gu, and R. Willett. “Adaptive Differentially Private Empirical Risk Minimization”. In: (2021). submitted.

[15] Yuming Chen, Daniel Sanz-Alonso, and Rebecca Willett. “Auto-differentiable Ensemble Kalman Filters”. In: arXiv preprint arXiv:2107.07687 (2021).

[16] Adrian Maldonado, Emil M. Constantinescu, Hong Zhang, Vishwas Rao, and Mihai Anitescu. “Trust-region approximation of extreme trajectories in power system dynamics”. In: Submitted to IEEE Transactions on Power Systems (2021).

[17] Zhewei Yao, Peng Xu, Fred Roosta, Stephen JWright, and MichaelWMahoney. “Inexact Newton-CG Algorithms With Complexity Guarantees”. In: arXiv preprint arXiv:2109.14016 (2021).

[18] Z. Ding, Q. Li, J. Lu, and S. J. Wright. “Random coordinate Langevin Monte Carlo“. In: arXiv preprint arXiv:2010.01405 (2020). In: Conference on Learning Theory. PMLR. 2021, pp. 1683-1710.

[19] Pratyush Kumar, James B Rawlings, and Stephen J Wright. “Industrial, large-scale model predictive control with structured neural networks“. In: Computers & Chemical Engineering 150 (2021), p. 107291.

[20] Zhiyan Ding, Shi Chen, Qin Li, and Stephen Wright. “Overparameterization of deep ResNet: zero loss and mean-field analysis“. In: arXiv preprint arXiv:2105.14417 (2021).

[21] Yue Xie and Stephen J Wright. “Complexity of Proximal augmented Lagrangian for nonconvex optimization with nonlinear equality constraints“. In: Journal of Scientific Computing 86.3 (2021), pp. 1-30.

[22] Chaobing Song, Stephen J Wright, and Jelena Diakonikolas. “Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums“. In: arXiv
preprint arXiv:2102.13643 (2021).

[23] Ke Chen, Qin Li, Jianfeng Lu, and Stephen J Wright. “A Low-Rank Schwarz Method for Radiative Transfer Equation With Heterogeneous Scattering Coefficient“. In: Multiscale Modeling & Simulation 19.2 (2021), pp. 775-801.

[24] Michael O’Neill and Stephen J Wright. “A Log-Barrier Newton-CG Method for Bound Constrained Optimization with Complexity Guarantees“. In: IMA Journal of Numerical Analysis 41.1 (2021), pp. 84-121.

[25] Cong Han Lim, Je rey T Linderoth, James R Luedtke, and Stephen J Wright. “Parallelizing Subgradient Methods for the Lagrangian Dual in Stochastic Mixed-Integer Programming“. In: Informs Journal on Optimization 3.1 (2021), pp. 1-22.

[26] Jingyi Wang, Nai-Yuan Chiang, and Cosmin G. Petra. “An asynchronous distributed-memory optimization solver for two-stage stochastic programming problems“. In: 2021 20th International Symposium on Parallel and Distributed Computing (ISPDC). 2021, pp. 33-40.

[27] Johannes J Brust and Mihai Anitescu. “Convergence Analysis of Fixed Point Chance Constrained Optimal Power Flow Problems“. In: arXiv preprint arXiv:2101.11740 (2021).

[28] Rui Chen and James R. Luedtke. “On Generating Lagrangian Cuts for Two-stage Stochastic Integer Programs“. In: arXiv preprint arXiv:2106.04023 (2021).

[29] Amanda Lenzi, Julie Bessac, and Mihai Anitescu. “Detecting Large Frequency Excursions in the Power Grid with Bayesian Decision Theory”. In: IEEE Power & Energy Society (2021). Submitted.

[30] Youngdae Kim, Francois Pacaud, Kibaek Kim, and Mihai Anitescu. “Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition“. In: arXiv preprint arXiv:2106.14995 (2021). Submitted to SISC.

[31] Sungho Shin, Mihai Anitescu, and Victor M Zavala. “Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs“. In: arXiv preprint arXiv:2101.03067 (2021).

[32] Sen Na, Mihai Anitescu, and Mladen Kolar. “A Fast Temporal Decomposition Procedure
for Long-horizon Nonlinear Dynamic Programming“. In: arXiv preprint arXiv:2107.11560 (2021).

[33] Francois Pacaud, Daniel Adrian Maldonado, Sungho Shin, Michel Schanen, and Mihai
Anitescu. “A Feasible Reduced Space Method for Real-Time Optimal Power Flow“. In: arXiv preprint arXiv:2110.02590 (2021).

[34] Daniel Adrian Maldonado, Michel Schanen, Francois Pacaud, and Mihai Anitescu. “Domain
Decomposition Preconditioners for Unstructured Network Problems in Parallel Vector Architectures“. In: 50th International Conference on Parallel Processing Workshop. 2021, pp. 1-5.

[35] Yian Chen and Mihai Anitescu. “Scalable Physics-based Maximum Likelihood Estimation using Hierarchical Matrices“. Submitted. Oct. 2021.

[36] Jeff Linderoth, Jose Nunez Ares, James Ostrowski, Fabrizio Rossi, and Stefano Smriglio. “Orbital Conflict: Cutting Planes for Symmetric Integer Programs“. In: INFORMS Journal on Optimization 3.2 (2021), pp. 139-153.

[37] Amanda Smith, Jeff Linderoth, and James Luedtke. “Optimization-Based Dispatching Policies for Open-Pit Mining“. In: Optimization and Engineering 22.3 (2021), pp. 1347-1387.

[38] Akhilesh Soni, Je  Linderoth, James Luedtke, and Fabian Rigterink. “Mixed-Integer Linear Programming for Scheduling Unconventional Oil Field Development“. Optimization Online. 2021.

[39] Arvind Ragathunan and Jeff Linderoth. “Reduced Space Completely Positive Representation Of Binary And Continuous Nonconvex Quadratic Programs”. Working paper. 2021.

[40] Michael L. Stein. “A weighted composite log-likelihood approach to parametric estimation of the extreme quantiles of Mdistribution“. In: Extremes (submitted).

[41] Bowen Li, Ruiwei Jiang, and Johanna L. Mathieu. “Integrating Unimodality into Distributionally Robust Optimal Power Flow”. Submitted to special issue of TOP. 2021.

[42] Minseok Ryu and Kibaek Kim. “A Privacy-Preserving Distributed Control of Optimal Power Flow”. In: IEEE Transactions on Power Systems (to appear) (2021).

[43] Youngdae Kim and Kibaek Kim. “Accelerated Computation and Tracking of AC Optimal
Power Flow Solutions Using GPUs“. (2021).

[44] Weiqi Zhang, Kibaek Kim, and Victor Zavala. “On the Tightness of the Lagrangian Dual Bound for the Alternating Current Optimal Power Flow Problem“. Working paper. (2021).

[45] Josh Arnold, Adam Christensen, and Michael Ferris. “Werewolf in London: Minding the Gap with User-Friendly Energy Optimization Tools Informing Policy Makers through the Energy Transition“. In: Accelerating the energy transition for all: evaluation’s role in effective policy making. Energy Evaluation Europe. 2021.

[46] Olivier Huber and Michael C. Ferris. “Reformulations for convex composite functions and their nested compositions”. 2021.

[47] Hamed Rahimian, Guzin Bayraksan, and Tito Homem-de-Mello. “Effective Scenarios in Multistage Distributionally Robust Optimization with a Focus on Total Variation Distance“. 2021.

[48] Jangho Park, Rebecca Stockbridge, and Guzin Bayraksan. “Variance reduction for sequential sampling in stochastic programming“. In: Annals of Operations Research 300 (1 2021), pp. 171-204.

[49] Chennan Zhou and Guzin Bayraksan. “Effective Scenarios in Two-Stage Distributionally Robust Optimization with Wasserstein Distance”. Working paper. 2021.

[50] Guzin Bayraksan, Daniel Faccini, Francesca Maggioni, and Ming Yang. “Bounds for Multistage Mixed-Integer Distributionally Robust Optimization”. To be submitted. 2021.

[51] Shaobu Wang, Renke Huang, Ning Zhou, and Zhenyu Huang. “Test for Non-synchronized Errors of State Estimation using Real Data”. In: IEEE Power Engineering Letters (2021). Submitted.

[52] Tong Ma, David A. Barajas-Solano, Ramakrishna Tipireddy, and Alexandre M. Tartakovsky. “Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids“. In: arXiv e-prints, http://arxiv.org/abs/2010.04591 (Oct. 2020). In: International Journal of Forecasting (2021). Resubmitted.

[53] C. L. Haley. “Missing-data multitaper coherence estimation“. In: IEEE Signal Processing
Letters 28 (Sept. 2021), pp. 1704-1708. Published.

[54] Johannes J. Brust, Sven Leyer, and Cosmin G. Petra. “Compact Representations of Structured BFGS Matrices“. In: Computational Optimization and Applications 80.1 (2021), pp. 55-88.

[55] F. E. Curtis, D. P. Robinson, C. W. Royer, and S. J. Wright. “Trust-region Newton-CG with strong second-order complexity guarantees for nonconvex optimization“. In: arXiv preprint arXiv:1912.04365 arXiv:1912.04365 (2019). Revised July 2020. In: SIAM Journal on Optimization 31.1 (2021), pp. 518-544.

[56] Ashutosh Mahajan, Sven Leyffer, Je Linderoth, James Luedtke, and Todd Munson. “Minotaur: A Mixed-Integer Nonlinear Optimization Toolkit“. In: Mathematical Programming Computation 13 (2021), pp. 308-338.

[57] Eli Towle and James Luedtke. “Intersection disjunctions for reverse convex sets“. In: Mathematics of Operations Research (2021).

[58] Peng Wang, Shaobu Wang, Renke Huang, and Zhenyu Huang. “Quantifying bounds of model gap for synchronous generators“. Submitted to IET control theory and applications. 2021.

[59] Shaobu Wang, Zhenyu Huang, Renke Huang, and R Fan. “Validation for Stochastic Models with Multiscale Uncertainties”. Submitted to IEEE Power Engineering Letters. 2021.

[60] Rohit Kannan and James Luedtke. “A stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs“. In: Mathematical Programming Computation (2021).

[61] Alberto Del Pia, Dion Gijswijt, Je Linderoth, and Haoran Zhu. “Integer Packing Sets Form a Well-Quasi Ordering“. In: Operations Research Letters 49.2 (2021), pp. 226-230

[62] A. Gleixner, G. Hendel, G. Gamrath, T. Achterberg, M. Bastubbe, T. Berthold, P. M. Christophel, K. Jarck, T. Koch, J. Linderoth, M. Lubbecke, H. D. Mittelmann, D. Ozyurt, T. K. Ralphs, D. Salvagnin, and Y. Shinano. “MIPLIB 2017: Data-Driven Compilation of the 6th Mixed-Integer Programming Library“. In: Mathematical Programming Computation 13.3 (2021), pp. 443-490.

[63] K. Sundar, H. Nagarajan, S. Wang, J. Linderoth, and R. Bent. “Piecewise Polyhedral Formulations for a Multilinear Term“. In: Operations Research Letters 49.1 (2021), pp. 144-149.

[64] Christopher J Geoga, Mihai Anitescu, and Michael L Stein. “Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data“. In: Environmetrics
32.5 (2021), e2670.

[65] Michael L. Stein. “Parametric models for distributions when interest is in extremes with an application to daily temperature“. In: Extremes 24 (2021), pp. 293-323. doi: https://doi.org/10.1007/s10687-020-00378-z.

[66] Jacob Roth, David A. Barajas-Solano, Panos Stinis, JonathanWeare, and Mihai Anitescu. “A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure“. In: Multiscale Modeling & Simulation 19.1 (2021), pp. 208-241.

[67] M. C. Ferris and A. B. Philpott. “Dynamic risked equilibrium“. In: Operations Research (2021).

[68] Yian Chen and Mihai Anitescu. “Scalable Gaussian Process Analysis for Implicit Physics-Based Covariance Models.” In: International Journal for Uncertainty Quantification 11.6
(2021).

[69] Vishwas Rao and Mihai Anitescu. “Efficient computation of extreme excursion probabilities for dynamical systems“. In: arXiv preprint arXiv:2001.11904 (2020). In: SIAM/ASA Journal on
Uncertainty Quantification 9.2 (2021), pp. 731-762.

[70] Amanda Lenzi, Julie Bessac, and Mihai Anitescu. “Power Grid Frequency Prediction Using Spatio-Temporal Modeling“. In: Journal of Statistical Analysis and Data Mining: The ASA Data Science Journal (2021), pp. 1-14.

[71] A. Bohm and S. J. Wright. “Variable smoothing for weak convex composite functions“. In: Journal of optimization theory and applications 188.3 (2021), pp. 628-649.

[72] Noah Rhodes, David Fobes, Carleton Coffrin, and Line Roald. “PowerModelsRestoration. jl: An Open-Source Framework for Exploring Power Network Restoration Algorithms“. In: Power Systems Computation Conference (PSCC) (2020). In: Electric Power Systems Research 190 (2021), p. 106736. issn: 0378-7796.

[73] Noah Rhodes, Lewis Ntaimo, and Line Roald. “Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offs“. In: IEEE Transactions on Power Systems 36.4 (2021), pp. 3118-3128