2019

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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/j.jcp.2019.06.010.
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 Opti- mization and Applications (Sept. 2019). issn: 1573-2894. doi: 10.1007/s10589- 019- 00127-4. url: https://doi.org/10.1007/s10589-019-00127-4.
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 pro- gramming 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 con- struction 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) C. Coffrin, D. Fobes, N. Rhodes, and L. Roald. PowerModelsRestoration.jl: An Open- Source Framework for Exploring Power Network Restoration Algorithms. Submitted to Power Systems Computation Conference (PSCC) 2020. 2019.
12) Xialiang Dou and Mihai Anitescu. “Distributionally robust optimization with correlated data from vector autoregressive processes”. In: Operations Research Letters 47.4 (2019), pp. 294–299.
13) Daniel Dylewsky, Xiu Yang, Alexandre Tartakovsky, and J Nathan Kutz. “Engineering structural robustness in power grid networks susceptible to community desynchroniza- tion”. In: Applied Network Science 4.1 (2019), p. 24.
14) M. C. Ferris and A. B. Philpott. “100% renewable energy with storage”. In: submitted to Operations Research (May 2019).
15) M. C. Ferris and A. B. Philpott. “Dynamic risked equilibrium”. In: Operations Research
(2019). To appear.
16) M. C. Ferris and A. B. Philpott. Electricity markets and renewable energy. SIAM News. Sept. 2019.
17) Christopher J Geoga, Mihai Anitescu, and Michael L Stein. “Scalable Gaussian process computations using hierarchical matrices”. In: Journal of Computational and Graphical Statistics (2019), pp. 1–11.
18) A. Gleixner, G. Hendel, G. Gamrath, T. Achterberg, M. Bastubbe, T. Berthold, P. M. Christophel, K. Jarck, T. Koch, J. Linderoth, M. Lu¨bbecke, H. D. Mittelmann, D. Ozyurt, T. K. Ralphs, D. Salvagnin, and Y. Shinano. MIPLIB 2017: Data-Driven Com- pilation of the 6th Mixed-Integer Programming Library. Submitted. 2019. url: http:
//www.optimization-online.org/DB%5C_HTML/2019/07/7285.html.
19) E. Glendenning, S. J. Wright, and Weinhold. F. “Efficient optimization of natural res- onance theory weightings and bond orders by Gram-based convex programming”. In: Journal of Computational Chemistry (2019). To appear.
20) M. Gu¨rbu¨zbalaban, A. Ozdaglar, N. D. Vanli, and S. J. Wright. “Randomness and per- mutations in coordinate descent methods”. In: Mathematical Programming, Series B (2019). To appear.
21) R. Kannan, L. Roald, and J. Luedtke. Stochastic DC Optimal Power Flow With Reserve Saturation. Submitted to Power Systems Computation Conference (PSCC) 2020. 2019.
22) Rohit Kannan and James Luedtke. A stochastic approximation method for chance-constrained nonlinear programs. arXiv:1812.07066. 2019.
23) Cheolmin Kim, Kibaek Kim, Prasanna Balaprakash, and Mihai Anitescu. “Graph Con- volutional Neural Networks for Optimal Load Shedding under Line Contingency”. In: Proceedings of the IEEE Power & Energy Society General Meeting 2019. 2019.
24) 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.
25) Kim and M. C. Ferris. “Solving equilibrium problems using extended mathematical programming”. In: Mathematical Programming Computation (Mar. 2019).
26) Youngdae Kim, Sven Leyffer, and Todd Munson. MPEC methods for bilevel optimiza- tion problems. Tech. rep. Preprint ANL/MCS-P9195-0719. Mathematics and Computer Science Division, Argonne National Laboratory, 2019.
27) 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.
28) C.-p. Lee and S. J. Wright. “Inexact successive quadratic approximation for regularized optimization”. In: Computational Optimization and Applications 72 (2019), pp. 641–674.
29) 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.
30) 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.
31) 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].
32) 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.
33) Michael O’Neill and Stephen J. Wright. A Log-Barrier Newton-CG Method for Bound Constrained Optimization with Complexity Guarantees. Submitted, arXiv:1904.03563. 2019.
34) 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.
35) J. Park, D. Love, and G. Bayraksan. A Multistage Distributionally Robust Optimization Approach to Water Allocation under Climate Uncertainty. Tech. rep. The Ohio State University, 2019.
36) A. Pen˜a-Ordieres, D. Molzahn, L. Roald, and A. Waechter. DC Optimal Power Flow with Joint Chance Constraints. In preparation. 2019.
37) Alejandra Pen˜a-Ordieres, James Luedtke, and Andreas Waechter. Solving chance-constrained problems via a smooth sample-based 40)nonlinear approximation. Submitted to SIAM Jour- nal on Optimization. May 2019. url: arXiv:1905.07377.
38) Cosmin G. Petra. “A memory-distributed quasi-Newton solver for nonlinear program- ming problems with a small number of general constraints”. In: Journal of Parallel and Distributed Computing 133 (2019), pp. 337–348. issn: 0743-7315. doi: https://doi. org/10.1016/j.jpdc.2018.10.009.
39) 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.
40) Cosmin G. Petra and Florian A. Potra. “A homogeneous model for monotone mixed horizontal linear complementarity problems”. In: Computational Optimization and Ap- plications 72.1 (Jan. 2019), pp. 241–267. issn: 1573-2894. doi: 10.1007/s10589-018- 0035-x. url: https://doi.org/10.1007/s10589-018-0035-x.
41) A. Del Pia, J. Linderoth, and H. Zhu. Cutting Planes for Extended Formulations of Linear Programs with Complementarity Constraints. Working Paper. 2019.
42) 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.
43) J.L. Pulsipher and V.M. Zavala. A Scalable Stochastic Programming Approach for the Design of Flexible Systems. Under Review. 2019.
44) 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 (2019), pp. 854–868. doi: 10.1016/j.ejor.2019.06.036.
45) H. Rahimian, G. Bayraksan, and T. Homem-de-Mello. “Identifying effective scenarios in distributionally robust stochastic programs with total variation distance”. In: Mathe- matical Programming 173.1-2 (2019), pp. 393–430.
46) H. Rahimian, G. Bayraksan, and T. Homem-de-Mello. Multistage Distributionally Robust Optimization with Total Variation Distance. Tech. rep. The Ohio State University, 2019.
47) 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.
48) C. W. Royer, Michael O’Neill, and Stephen J. Wright. “A Newton-CG algorithm with complexity guarantees for smooth unconstrained optimization”. In: Mathematical Pro- gramming (2019). doi: 10.1007/s10107-019-01362-7.
49) Michel Schanen, Daniel Adrian Maldonado, and Mihai Anitescu. “A Framework for Dis- tributed Approximation of Moments with Higher-Order Derivatives Through Automatic Differentiation”. In: International Conference on Computational Science. Springer. 2019, pp. 251–260.
50) Michael L. Stein. “Parametric models for distributions when interest is in extremes with an application to daily temperature”. In: Submitted to Extremes (2019).
51) Michael L. Stein. “Some statistical issues in climate science”. In: Statistical Science, in press (2019).
52) K. Sundar, H. Nagarajan, S. Wang, J. Linderoth, and R. Bent. Piecewise Polyhedral Formulations for a Multilinear Term. Submitted. 2019.
53) Alexandre Tartakovsky and Ramakrishna Tipireddy. “Physics-informed Machine Learn- ing 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.
54) Eli Towle and James Luedtke. Intersection disjunctions for reverse convex sets. Submit- ted to Mathematics of Operations Research. 2019.
55) 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.
56) Shaobu Wang, Zhenyu Huang, Renke Huang, and R Fan. “Validation for Stochastic Models with Multiscale Uncertainties”. In: Submitted to IEEE Power Engineering Letters (2019).
57) S. J. Wright and C.-p. Lee. Analyzing random permutations for cyclic coordinate descent. Technical Report arXiv:1706.00908. University of Wisconsin-Madison, Feb. 2019.
58) 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.
59) Y. Xie and S. J. Wright. Complexity of proximal augmented Lagrangian for nonconvex optimization with nonlinear equality constraints. Technical Report arrXiv:1908.00131. University of Wisconsin-Madison, Sept. 2019.
60) Wanting Xu and Mihai Anitescu. “Exponentially accurate temporal decomposition for long-horizon linear-quadratic dynamic optimization”. In: SIAM Journal on Optimization 28.3 (2018), pp. 2541–2573.
61) Wanting Xu and Mihai Anitescu. “Exponentially Convergent Receding Horizon Strategy For Constrained Optimal Control”. In: Vietnam Journal of Mathematics (). to appear.
62) 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.
63) H. Zhang, S. Ericksen, C.-p. Lee, G. Ananiev, N. Wlodarchak, P. Yu, J. C. Mitchell, A. Gitter, S. J. Wright, Hoffmann. F. M., S. A. Wildman, and M. A. Newton. “Predicting ki- nase inhibitors using bioactivity matrix derived informer sets,” in: PLOS Computational Biology (2019). To appear.
64) C. Zhou and G. Bayraksan. Using Effective Scenarios to Accelerate Decomposition Al- gorithms for Two-Stage Distributionally Robust Optimization with Total Variation Dis- tance. Tech. rep. The Ohio State University, 2019.
65) H. Zhu, A. Del Pia, and J. Linderoth. Integer Packing Sets Form a Well-Quasi Ordering. Working Paper. 2019.