Publications

2022
  1. Andres Felipe Ramirez-Arias, Alberto J. Lamadrid, & Carlos Felipe Valencia-Arboleda (2022). Virtual Power Plant Day Ahead Energy Unit Commitment Submission. In 2022 55th Hawaii International Conference on System Sciences.
  2. Mertcan Yetkin, Brandon R. Augustino, Alberto J. Lamadrid, & Lawrence V. Snyder. (2021). Co-optimizing the Smart Grid and Electric Public Transit Bus System.
  3. Tomás Tapia, Álvaro Lorca, Daniel Olivares, Mat\’\ias Negrete-Pincetic, & Alberto J. Lamadrid L (2021). A robust decision-support method based on optimization and simulation for wildfire resilience in highly renewable power systems. European Journal of Operational Research, 294(2), 723-733.
  4. Bining Zhao, Alberto J. Lamadrid, Rick S. Blum, & Shalinee Kishore (2021). A coordinated scheme of electricity-gas systems and impacts of a gas system FDI attacks on electricity system. International Journal of Electrical Power and Energy Systems, 131, 107060.
  5. Diana Mitsova, Ann-Margaret Esnard, Alka Sapat, Alberto J. Lamadrid, Monica Escaleras, & Catherine Velarde-Perez (2021). Effects of Infrastructure Service Disruptions Following Hurricane Irma: Multilevel Analysis of Postdisaster Recovery Outcomes. Natural Hazards Review, 22(1), 04020055.
  6. Del Pia, A., & Khajavirad, A. (2022). Rank-one Boolean tensor factorization and the multilinear polytope. arXiv:2202.07053.
  7. B. Ramos, D. Pinho, D. Martins, A. I. F. Vaz, & L. N. Vicente (2022, to appear). Optimal 3D printing of complex objects in a 5-axis printer. opteng.
  8. S. Liu, & L. N. Vicente (2022, to appear). The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning. AOR.
  9. L. Song, & L. N. Vicente (2022, to appear). Modeling Hessian-vector products in nonlinear optimization: New Hessian-free methods. imana.
  10. Rangarajan, S. (2022). Towards a chemistry-informed paradigm for designing molecules. Current Opinion in Chemical Engineering, 35, 100717.
  11. Chen, K., Tian, H., Li, B., & Rangarajan, S. (2022). A chemistry-inspired neural network kinetic model for oxidative coupling of methane from high-throughput data. AIChE Journal, e17584.
  12. Bolusani, S., and T.K., Ralphs. “A Framework for Generalized Benders’ Decomposition and Its Application to Multilevel Optimization”.Mathematical Programming Series B (2022).
    Xueyu, Shi, Oleg A., Prokopyev, and Ted K., Ralphs. 2022. “Mixed Integer Bilevel Optimization with k-optimal Follower: A Hierarchy of Bounds.” CORAL Laboratory Report 20T-012-R1, Lehigh University, 2022.
2021
  1. Del Pia, A., & Khajavirad, A. (2021). The running intersection relaxation of the multilinear polytope. Mathematics of Operations Research, 46, 1008–1037.
  2. De Rosa, A., & Khajavirad, A. (2021). The ratio-cut polytope and K-means clustering. SIAM Journal on Optimization, to appear.
  3. De Rosa, A., & Khajavirad, A. (2021). Efficient joint object matching via linear programming. arXiv:2108.11911.
  4. Tianyu Ding, Zhihui Zhu, Manolis Tsakiris, René Vidal, & Daniel P. Robinson (2021). Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms. In AISTATS.
  5. Tianyu Ding, Zhihui Zhu, René Vidal, & Daniel P. Robinson (2021). Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach. In ICML.
  6. Guilherme Fran\cca, Daniel P. Robinson, & René Vidal (2021). Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization. Physical Review E, 103(5).
  7. Mustafa D. Kaba, Chong You, Daniel P. Robinson, Enrique Mallada, & René Vidal (2021). A Nullspace Property for Subspace-Preserving Recovery. ICML.
  8. Mertcan Yetkin, Sudharsan Kalidoss, Frank E. Curtis, Lawrence V. Snyder, & Arindam Banerjee (2021). Practical optimal control of a wave-energy converter in regular wave environments. Renewable Energy, 171, 1382–1394.
  9. Chenxin Ma, Martin Jaggi, Frank E. Curtis, Nathan Srebro, & Martin Taká\vc (2021). An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine Learning. Optimization Methods and Software, 36(1), 20–44.
  10. Frank E. Curtis, & Daniel P. Robinson (2021). Regional Complexity Analysis of Algorithms for Nonconvex Smooth Optimization. Mathematical Programming, 187, 579–615.
  11. Frank E. Curtis, Daniel P. Robinson, Clément W. Royer, & Stephen J. Wright (2021). Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization. SIAM Journal on Optimization, 31(1), 518–544.
  12. Albert S. Berahas, Frank E. Curtis, Daniel P. Robinson, & Baoyu Zhou (2021). Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. SIAM Journal on Optimization, 31(2), 1352–1379.
  13. Albert S. Berahas, Frank E. Curtis, Michael J. O’Neill, & Daniel P. Robinson (2021). A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear Equality Constrained Optimization with Rank-Deficient Jacobians. https://arxiv.org/abs/2106.13015.
  14. Frank E. Curtis, Daniel P. Robinson, & Baoyu Zhou (2021). Inexact Sequential Quadratic Optimization for Minimizing a Stochastic Objective Function Subject to Deterministic Nonlinear Equality Constraints. https://arxiv.org/abs/2107.03512.
  15. Frank E. Curtis, Daniel K. Molzahn, Shenyinying Tu, Andreas Waechter, Ermin Wei, & Elizabeth Wong (2021). A Decomposition Algorithm for Large-Scale Security-Constrained AC Optimal Power Flow. https://arxiv.org/abs/2110.01737.
  16. Frank E. Curtis, Michael J. O’Neill, & Daniel P. Robinson (2021). Worst-Case Complexity of an SQP Method for Nonlinear Equality Constrained Stochastic Optimization. https://arxiv.org/abs/2112.14799.
  17. Shehadeh, K., Wang, H., & Zhang, P. (2021). Fleet sizing and allocation for on-demand last-mile transportation systems. Transportation Research Part C: Emerging Technologies, 132, 103387.
  18. Shehadeh, K. (2021). Data-Driven distributionally robust surgery planning in flexible operatingrooms over a wasserstein ambiguity. arXiv preprint arXiv:2103.15221.
  19. Tsang, M., & Shehadeh, K. (2021). Distributionally robust home service routing and appointment scheduling with random travel and service times. arXiv preprint arXiv:2105.01725.
  20. Shehadeh, K. (2021). A distributionally robust optimization approach for a stochastic mobile facility routing and scheduling problem. arXiv preprint arXiv:2009.10894.
  21. Shehadeh, K., & Snyder, L. (2021). Equity in stochastic healthcare facility location. https://arxiv.org/abs/2112.03760.
  22. Shehadeh, K., & Tucker, E. (2021). A Distributionally robust optimization approach for location and inventory prepositioning of disaster relief supplies. arXiv preprint arXiv:2012.05387.
  23. Shehadeh, K., & Sanci, E. (2021). Distributionally robust facility location with bimodal random demand. Computers and Operations Research (accepted, forthcoming).
  24. Shehadeh, K., & Padman, R. (2021). A distributionally robust optimization approach for stochastic elective surgery scheduling with limited intensive care unit capacity. European Journal of Operational Research, 290(3), 901–913.
  25. Shehadeh, K., Cohn, A., & Jiang, R. (2021). Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times. Naval Research Logistics (NRL), 68(1), 89–111.
  26. T. Giovannelli, G. Kent, & L. N. Vicente (2021). Bilevel stochastic methods for optimization and machine learning: Bilevel stochastic descent and DARTS [White paper]. Department of Industrial and Systems Engineering, Lehigh University.
  27. S. Liu, & L. N. Vicente (2021). The Sharpe predictor for fairness in machine learning [White paper]. Department of Industrial and Systems Engineering, Lehigh University.
  28. A. S. Berahas, O. Sohab, & L. N. Vicente (2021). Full-low evaluation methods for derivative-free optimization [White paper]. Department of Industrial and Systems Engineering, Lehigh University.
  29. S. Liu, & L. N. Vicente (2021). A stochastic alternating balance k-means algorithm for fair clustering [White paper]. Department of Industrial and Systems Engineering, Lehigh University.
  30. Mertcan Yetkin, Sudharsan Kalidoss, Frank E. Curtis, Lawrence V. Snyder, & Arindam Banerjee (2021). Practical optimal control of a wave-energy converter in regular wave environments. Renewable Energy, 171, 1382-1394.
  31. Mertcan Yetkin, Brandon Augustino, Alberto J. Lamadrid, & Lawrence V. Snyder (2021). Co-optimizing the Smart Grid and Electric Public Transit Bus System [White paper]. Lehigh University.
  32. Zhuo, Y., Snyder, L., Rick Blum, Shalinee Kishore, & Parv Venkitasubramaniam (2021). Information Attacks under Cyber-secure Market Interactions in Power Systems [White paper]. Lehigh University.
  33. Tian, H., & Rangarajan, S. (2021). Machine-Learned Corrections to Mean-Field Microkinetic Models at the Fast Diffusion Limit. The Journal of Physical Chemistry C, 125(37), 20275–20285.
  34. Yin, H., Lee, J., Kong, X., Hartvigsen, T., & Xie, S. (2021). Energy-Efficient Models for High-Dimensional Spike Train Classification using Sparse Spiking Neural Networks. In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining.
  35. Chen, C., Yifan, S., Ma, G., Zhang, X., Kong, X., & Xie, S. (2021). Self-learn to Explain Siamese Networks Robustly. In ICDM.
  36. Liu, Y., Chen, C., Liu, Y., Zhang, X., & Xie, S. (2021). Multi-objective Explanations of GNN Predictions. In ICDM.
  37. Burkholder, K., Kwock, K., Xu, S., Liu, J., & Xie, S. (2021). Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection. In CIKM.
  38. Bulut, A., and T.K., Ralphs. “On the Complexity of Inverse Mixed Integer Linear Optimization”.SIAM Journal on Optimization 31 (2021): 3014–3043.
2020
  1. Bining Zhao, Alberto J. Lamadrid, Rick S. Blum, & Shalinee Kishore (2020). A trilevel model against false gas-supply information attacks in electricity systems. Electric Power Systems Research, 189, 106541.
  2. Diana Mitsova, Alka Sapat, Ann-Margaret Esnard, & Alberto J. Lamadrid (2020). Evaluating the Impact of Infrastructure Interdependencies on the Emergency Services Sector and Critical Support Functions Using an Expert Opinion Survey. Journal of Infrastructure Systems, 26(2), 04020015.
  3. Del Pia, A., Khajavirad, A., & Sahinidis, N. (2020). On the impact of running intersection inequalities for globally solving polynomial optimization problems. Mathematical programming computation, 12(2), 165–191.
  4. Del Pia, A., Khajavirad, A., & Kunisky, D. (2020). Linear programming and community detection. arXiv:2006.03213.
  5. Guilherme Fran\cca, Daniel P. Robinson, Jeremias Sulam, & René Vidal (2020). Conformal Symplectic and Relativistic Optimization. Conference on Neural Information Processing Systems (NeurIPS).
  6. Tianjiao Ding, Yuchen Yang, Zhihui Zhu, Daniel P. Robinson, René Vidal, Laurent Kneip, & Manolis Tsakiris (2020). Homography Estimation via Dual Principal Component Pursuit. In CVPR.
  7. Guilherme Fran\cca, Jeremias Sulam, Daniel P. Robinson, & René Vidal (2020). Conformal Symplectic and Relativistic Optimization. Journal of Statistical Mechanics: Theory and Experiment.
  8. Philip E. Gill, Vyacheslav Kungurtsev, & Daniel P. Robinson (2020). A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization. SIOPT, 30(2), 1067–1093.
  9. Chong You, Chi Li, Daniel P. Robinson, & René Vidal (2020). Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces. TPAMI.
  10. Daniel P. Robinson, René Vidal, & Chong You (2020). Basis Pursuit and Orthogonal Matching Pursuit for Subspace-preserving Recovery: Theoretical Analysis. JMLR.
  11. Wenbo Gao, Donald Goldfarb, & Frank E. Curtis (2020). ADMM for Multiaffine Constrained Optimization. Optimization Methods and Software, 35(2), 257–303.
  12. James V. Burke, Frank E. Curtis, Adrian S. Lewis, Michael L. Overton, & Lucas E. A. Sim\~oes (2020). Gradient Sampling Methods for Nonsmooth Optimization. Journal is required!, 201–225.
  13. Frank E. Curtis, Daniel P. Robinson, & Baoyu Zhou (2020). A Self-Correcting Variable-Metric Algorithm Framework for Nonsmooth Optimization. IMA Journal of Numerical Analysis, 40(2), 1154–1187.
  14. Frank E. Curtis, & Katya Scheinberg (2020). Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms. IEEE Signal Processing Magazine, 37(5), 32–42.
  15. James V. Burke, Frank E. Curtis, Hao Wang, & Jiashan Wang (2020). Inexact Sequential Quadratic Optimization with Penalty Parameter Updates within the QP Solver. SIAM Journal on Optimization, 30(3), 1822–1849.
  16. Frank E. Curtis, & Rui Shi (2020). A Fully Stochastic Second-Order Trust Region Method. Optimization Methods and Software, https://doi.org/10.1080/10556788.2020.1852403.
  17. Frank E. Curtis, & Minhan Li (2020). Gradient Sampling Methods with Inexact Subproblem Solutions and Gradient Aggregation. https://arxiv.org/abs/2005.07822.
  18. Frank E. Curtis, Yutong Dai, & Daniel P. Robinson (2020). A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer. https://arxiv.org/abs/2007.14951.
  19. Shehadeh, K., Cohn, A., & Jiang, R. (2020). A distributionally robust optimization approach for outpatient colonoscopy scheduling. European Journal of Operational Research, 283(2), 549–561.
  20. S. Liu, & L. N. Vicente (2020). Accuracy and fairness trade-offs in machine learning: A stochastic multi-objective approach [White paper]. Department of Industrial and Systems Engineering, Lehigh University.
  21. S. Gratton, C. W. Royer, & L. N. Vicente (2020). A decoupled first/second-order steps technique for nonconvex nonlinear unconstrained optimization with improved complexity bounds. mprog, 179, 195–222.
  22. Afshin OroojlooyJadid, Mohammadreza Nazari, Martin Taká\vc, & Snyder, L. (2020). A Deep Q-Network for the Beer Game: Reinforcement Learning for Inventory Optimization. Manufacturing and Service Operations Management, \em forthcoming.
  23. Kostas Hatalis, Chengbo Zhao, Parv Venkitasubramaniam, Larry Snyder, Shalinee Kishore, & Rick S. Blum (2020). Modeling and Detection of Future Cyber-Enabled DSM Data Attacks. Energies, 13(17), 4331.
  24. OroojlooyJadid, A., Snyder, L., & Taká\vc, M. (2020). Applying deep learning to the newsvendor problem. IISE Transactions, 52(4), 444-463.
  25. Pirhooshyaran, M., Katya Scheinberg, & Lawrence V. Snyder (2020). Feature Engineering and Forecasting via Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks with Applications in Renewable Energy. Energy, 196, 117136.
  26. Pirhooshyaran, M., & Lawrence V. Snyder (2020). Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks. Ocean Engineering, 207, 107424.
  27. Pirhooshyaran, M., & Snyder, L. (2020). Simultaneous Decision Making for Stochastic Multi-Echelon Inventory Optimization with Deep Neural Networks as Decision Makers [White paper]. Lehigh University.
  28. Yao, J., Zhao, C., Venkitasubramaniam, P., Snyder, L., Kishore, S., & Blum, R. (2020). Data Injection Attack On Cyber-Enabled Demand-Side Management Feedback Loop [White paper]. Lehigh University.
  29. Paragian, K., Li, B., Massino, M., & Rangarajan, S. (2020). A computational workflow to discover novel liquid organic hydrogen carriers and their dehydrogenation routes. Molecular Systems Design & Engineering, 5(10), 1658–1670.
  30. Liu, Y., Chen, C., Liu, Y., Zhang, X., & Xie, S. (2020). Shapley Values and Meta-Explanations for Probabilistic Graphical Model Inference. In 29TH ACM International Conference on Information and Knowledge Management.
  31. Yingtong, D., Ma, G., Yu, P., & Xie, S. (2020). Robust Detection of Adaptive Spammers by Nash Reinforcement Learning. In KDD.
  32. Tahernejad, S., and T.K., Ralphs. 2020. “Valid Inequalities for Mixed Integer Bilevel Optimization Problems.” CORAL Laboratory Report 20T-013, 2020.
  33. Tahernejad, S., T.K., Ralphs, and S.T., DeNegre. “A Branch-and-Cut Algorithm for Mixed Integer Bilevel Linear Optimization Problems and Its Implementation”.Mathematical Programming Computation 12 (2020): 529–568.
  34. Bolusani, S., S., Coniglio, and S., Ralphs. “A Unified Framework for Multistage Mixed Integer Linear Optimization”.Bilevel Optimization: Advances and Next Challenges (2020): 513–560.
Before 2020
  1. Li, B., & Rangarajan, S. (2019). Designing compact training sets for data-driven molecular property prediction through optimal exploitation and exploration. Molecular Systems Design & Engineering, 4(5), 1048–1057.
  2. Tian, H., & Rangarajan, S. (2019). Predicting adsorption energies using multifidelity data. Journal of chemical theory and computation, 15(10), 5588–5600.
  3. Ralphs, T.K., Y., Shinano, T., Berthold, and T., Koch. Parallel Solvers for Mixed Integer Linear Programing.Springer Berlin / Heidelberg, 2018.
  4. Belotti, P., J.C., G\’oez, T.K., P\’olik, and T., Terlaky. “A Complete Characterization of Disjunctive Conic Cuts for Mixed Integer Second Order Cone Optimization”.Discrete Optimization 24 (2016).
  5. Hassanzadeh, A., and T.K., Ralphs. 2014. “On the Value Function of a Mixed Integer Linear Optimization Problem and an Algorithm for Its Construction.” CORAL Laboratory, Lehigh University, 2014.
  6. Hassanzadeh, A., and T.K., Ralphs. 2014. “A Generalization of Benders’ Algorithm for Two-Stage Stochastic Optimization Problems with Mixed Integer Recourse.” CORAL Laboratory, Lehigh University, 2014.
  7. Lodi, A., T.K., Ralphs, and G., Woeginger. “Bilevel Programming and the Separation Problem”.Mathematical Programming 148 (2014): 437–458.
  8. Koch, T., T.K., Ralphs, and Y., Shinano. “Could We Use a Million Cores to Solve an Integer Program?”.Mathematical Methods of Operations Research 76 (2012): 67–93.
  9. Güzelsoy, M., and T.K., Ralphs. “Duality for Mixed-Integer Linear Programs”.International Journal of Operations Research 4 (2007): 118–137.