Publications in 2019

[1] D. A. Barajas-Solano and Z. Huang. “Stochastic Resonance When Uncertainty Meets Dynamics”. In: Notices of the American Mathematical Society 66.1 (2019), pp. 106–107.

[2] D. A. Barajas-Solano and A. M. Tartakovsky. “Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients”. In: J. Comput. Phys. 395 (2019), pp. 247–262. doi: 10.1016/

[3] G´abor Braun, Sebastian Pokutta, Dan Tu, and Stephen Wright. “Blended Conditonal Gradients”. In: Proceedings of the 36th International Conference on Machine Learning. Ed. by Kamalika Chaudhuri and Ruslan Salakhutdinov. Vol. 97. Proceedings of Machine Learning Research. Long Beach, California, USA: PMLR, Sept. 2019, pp. 735–743.

[4] J. J. Brust, R. F. Marcia, and C. G. Petra. “Large-Scale Quasi-Newton Trust-Region Methods with Low-Dimensional Linear Equality Constraints”. In: Computational Optimization and Applications (Sept. 2019). issn: 1573-2894.

[5] J.J. Brust, R.F. Marcia, and C.G Petra. “Computationally Efficient Decompositions of Oblique Projection Matrices”. Technical Report 2019-2. Argonne National Laboratory, 2019.

[6] W. Chang, M. C. Ferris, Y. Kim, and T. F. Rutherford. “Solving stochastic dynamic programming problems: a mixed complementarity approach”. In: Computational Economics (2019). doi: 10.1007/s10614-019-09921-y.

[7] Z. Charles, S. Rajput, S. J. Wright, and D. Papailiopoulos. “Convergence and margin of adversarial training on separable data“. Technical Report arXiv:1905.09209. University of Wisconsin-Madison, May 2019.

[8] K. Chen, Q. Li, J. Lu, and S. J. Wright. “A low-rank Schwarz method for radiative trans- port equation with heterogeneous scattering coefficient“. Technical Report arXiv:1906.02176. University of Wisconsin-Madison, 2019.

[9] K. Chen, Q. Li, J. Lu, and S. J. Wright. “Randomized sampling for basis function construction in generalized finite element methods”. In: Multiscale Modeling and Simulation (2019). To appear.

[10] K. Chen, Q. Li, K. Newton, and S. J. Wright. “Structured random sketching for PDE inverse problems“. Technical Report arXiv:1909.11290. University of Wisconsin-Madison, Sept. 2019.

[11] Xialiang Dou and Mihai Anitescu. “Distributionally robust optimization with correlated data from vector autoregressive processes”. Technical Report arXiv:1909.03433. In: Operations Research Letters 47.4 (2019), pp. 294–299.

[12] Daniel Dylewsky, Xiu Yang, Alexandre Tartakovsky, and J Nathan Kutz. “Engineering structural robustness in power grid networks susceptible to community desynchronization”. In: Applied Network Science 4.1 (2019), p. 24.

[13] M. C. Ferris and A. B. Philpott. “100% renewable energy with storage”. In: submitted to Operations Research (May 2019).

[14] M. C. Ferris and A. B. Philpott. “Dynamic risked equilibrium“. In: Operations Research (2019). Accepted.

[15] M. C. Ferris and A. B. Philpott. “Electricity markets and renewable energy”. SIAM News. Sept. 2019.

[16] E. Glendenning, S. J. Wright, and Weinhold. F. “Efficient optimization of natural resonance theory weightings and bond orders by Gram-based convex programming“. In: Journal of Computational Chemistry 40.23 (2019), pp. 2028-2035.

[17] Cheolmin Kim, Kibaek Kim, Prasanna Balaprakash, and Mihai Anitescu. “Graph convolutional neural networks for optimal load shedding under line contingency”. In: 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE. 2019, pp. 1–5.

[18] Kibaek. Kim, Cosmin G. Petra, and Victor M. Zavala. “An Asynchronous Bundle-Trust- Region Method for Dual Decomposition of Stochastic Mixed-Integer Programming”. In: SIAM Journal on Optimization 29.1 (2019), pp. 318–342. doi: 10.1137/17M1148189.

[19] Kim and M. C. Ferris. “Solving equilibrium problems using extended mathematical programming”. Technical Report arXiv:1806.02255. In: Mathematical Programming Computation (Mar. 2019).

[20] Youngdae Kim, Sven Leyffer, and Todd Munson. “MPEC methods for bilevel optimization problems”. Tech. rep. Preprint ANL/MCS-P9195-0719. Mathematics and Computer Science Division, Argonne National Laboratory, 2019.

[21] Youngseok Kim, Peter Carbonetto, Matthew Stephens, and Mihai Anitescu. “A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming”. In: Journal of Computational and Graphical Statistics (2019). To appear; also arXiv preprint arXiv:1806.01412.

[22] C.-p. Lee and S. J. Wright. “Inexact successive quadratic approximation for regularized optimization”. In: Computational Optimization and Applications 72 (2019), pp. 641–674.

[23] C.-p. Lee and S. J. Wright. “Inexact variable metric stochastic block-coordinate descent for regularized optimization“. Technical Report arXiv:1807.09146. University of Wisconsin- Madison, July 2019.

[24] Ching-Pei Lee and Stephen Wright. “First-Order Algorithms Converge Faster than O(1/k) on Convex Problems”. In: Proceedings of the 36th International Conference on Machine Learning. Ed. by Kamalika Chaudhuri and Ruslan Salakhutdinov. Vol. 97. Proceedings of Machine Learning Research. Long Beach, California, USA: PMLR, Sept. 2019, pp. 3754–3762.

[25] Tong Ma, Renke Huang, David Barajas-Solano, Ramakrishna Tipireddy, and Alexandre M. Tartakovsky. “Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression”. In: (2019). arXiv: 1910.03783 [cs.LG].

[26] R. Mazumder, S. J. Wright, and A. Zheng. “Computing estimators of Dantzig selector type via column and constraint generation“. Tech. rep. arXiv:1908.06515. University of Wisconsin-Madison, 2019.

[27] B. Park, M. C. Ferris, and C. L. DeMarco. “Benefits of sparse tableau approach for power system analysis and design”. In: IEEE Transactions on Power Systems (May 2019). doi: 10.1109/TPWRS.2019.2916719.

[28] Cosmin G. Petra. “A memory-distributed quasi-Newton solver for nonlinear programming problems with a small number of general constraints”. In: Journal of Parallel and Distributed Computing 133 (2019), pp. 337–348. issn: 0743-7315. doi:

[29] Cosmin G. Petra, Naiyuan. Chiang, and Mihai. Anitescu. “A Structured Quasi-Newton Algorithm for Optimizing with Incomplete Hessian Information”. In: SIAM Journal on Optimization 29.2 (2019), pp. 1048–1075. doi: 10.1137/18M1167942.

[30] Cosmin G. Petra and Florian A. Potra. “A homogeneous model for monotone mixed horizontal linear complementarity problems”. In: Computational Optimization and Applications 72.1 (Jan. 2019), pp. 241–267. issn: 1573-2894. doi: 10.1007/s10589-018-0035-x.

[31] A. Del Pia, J. Linderoth, and H. Zhu. “Cutting Planes for Extended Formulations of Linear Programs with Complementarity Constraints”. Working Paper. 2019.

[32] J.L. Pulsipher, D. Rios, and V.M. Zavala. “A Computational Framework for Quantifying and Analyzing System Flexibility”. In: Computers & Chemical Engineering (2019). In Press.

[33] Joshua L Pulsipher and Victor M Zavala. “A scalable stochastic programming approach for the design of exible systems“. In: Computers & Chemical Engineering 128 (2019), pp. 69 – 76.

[34] H. Rahimian, G. Bayraksan, and T. Homem-de-Mello. “Controlling Risk and Demand Ambiguity in Newsvendor Models“. In: European Journal on Operational Research 279.3 (Dec. 2019), pp. 854 – 868. doi: 10.1016/j.ejor.2019.06.036.

[35] H. Rahimian, G. Bayraksan, and T. Homem-de-Mello. “Identifying effective scenarios in distributionally robust stochastic programs with total variation distance”. In: Mathematical Programming 173.1-2 (2019), pp. 393–430.

[36] H. Rahimian, G. Bayraksan, and T. Homem-de-Mello. “Multistage Distributionally Robust Optimization with Total Variation Distance”. Tech. rep. The Ohio State University, 2019.

[37] Vishwas Rao, Kibaek Kim, Michel Schanen, Daniel A. Maldonado, Cosmin G. Petra, and Mihai Anitescu. “A Multiperiod Optimization-Based Metric of Grid Resilience”. In: Proceedings of the IEEE Power & Energy Society General Meeting 2019. 2019.

[38] C. W. Royer, Michael O’Neill, and Stephen J. Wright. “A Newton-CG algorithm with complexity guarantees for smooth unconstrained optimization”. In: Mathematical Programming (2019). doi: 10.1007/s10107-019-01362-7.

[39] Michel Schanen, Daniel Adrian Maldonado, and Mihai Anitescu. “A Framework for Distributed Approximation of Moments with Higher-Order Derivatives Through Automatic Differentiation”. In: International Conference on Computational Science. Springer. 2019, pp. 251–260.

[40] Alexandre Tartakovsky and Ramakrishna Tipireddy. “Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids”. In: Proceedings of the 52nd Hawaii International Conference on System Sciences. 2019.

[41] Eli Towle and James Luedtke. “Intersection disjunctions for reverse convex sets”. Submitted to Mathematics of Operations Research. 2019.

[42] Shaobu Wang and Zhenyu Huang. “An alternative approach for MLE calculation in non-linear continuous dynamic systems”. In: Nonlinear Dynamics 95.3 (Feb. 2019), pp. 2591 – 2603.

[43] Shaobu Wang, Zhenyu Huang, Renke Huang, and R Fan. “Validation for Stochastic Models with Multiscale Uncertainties”. In: Submitted to IEEE Power Engineering Letters (2019).

[44] Stephen J. Wright. “Efficient Convex Optimization for Linear MPC”. In: Handbook of Model Predictive Control. Ed. by Saˇsa V. Rakovi´c and William S. Levine. Cham: Springer International Publishing, 2019, pp. 287–303.

[45] Wanting Xu and Mihai Anitescu. “Exponentially Convergent Receding Horizon Strategy For Constrained Optimal Control”. In: Vietnam Journal of Mathematics (2019). to appear.

[46] F. Zeng, I. Turner, K. Burrage, and S. J. Wright. “A discrete least squares collocation method for two- dimensional nonlinear time-fractional partial differential equations”. In: Journal of Computational Physics 394 (2019), pp. 177–199.

[47] C. Zhou and G. Bayraksan. “Using Effective Scenarios to Accelerate Decomposition Algorithms for Two-Stage Distributionally Robust Optimization with Total Variation Distance”. Tech. rep. The Ohio State University, 2019.

[48] A. Bircheld, R. D. Christie, C. Corin, M. Ferris, C. Josz, R. Korab, B. Leseuitre, D. Molzahn, T. J. Overbye, and R. Zimmerman. “The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms“. Tech. rep. IEEE PES PGLib Task Force, 2019.

[49] Jing Li and Panos Stinis. “Model reduction for a power grid model“. In: arXiv e-prints, arXiv:1912.12163 (Dec. 2019), arXiv:1912.12163. arXiv: 1912.12163 [eess.SY].

[50] Jacob Roth, David A. Barajas-Solano, Panos Stinis, JonathanWeare, and Mihai Anitescu. “A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure“. In: arXiv e-prints, arXiv:1912.08081 (Dec. 2019). Submitted to SIAM Multiscale Modeling and Simulation, arXiv:1912.08081. arXiv: 1912.08081 [eess.SY].

[51] Xiu Yang, David Barajas-Solano, Guzel Tartakovsky, and Alexandre M. Tartakovsky. “Physics-informed CoKriging: A Gaussian-process-regression-based multidelity method for data-model convergence“. In: Journal of Computational Physics 395 (2019), pp. 410 – 431.

[52] H. Zhang, S. Ericksen, C.-p. Lee, G. Ananiev, N. Wlodarchak, P. Yu, J. C. Mitchell, A. Gitter, S. J. Wright, Homann. F. M., S. A. Wildman, and M. A. Newton. “Predicting kinase inhibitors using bioactivity matrix derived informer sets,” in: PLOS Computational Biology (2019).