Jim Luedtke

Professor
Department of Industrial and Systems Engineering
Discovery Fellow, Wisconsin Institute for Discovery
University of Wisconsin-Madison
Madison, WI 53706

jim.luedtke at wisc dot edu


Education:


Research Interests:


Journal Papers:

  1. R. Kannan, G. Bayraksan, and J. Luedtke, "Residuals-based distributionally robust optimization with covariate information", Mathematical Programming, Online First, 2023. Preprint.
  2. R. Chen and J. Luedtke, "Sparse multi-term disjunctive cuts for the epigraph of a function of binary variables", Mathematical Programming, Online First, 2023. Preprint.
  3. M. Daryalal, M. Bodur, and J. Luedtke, "Lagrangian dual decision rules for multistage stochastic mixed integer programming", Operations Research, Articles in Advance, (2022). Preprint.
  4. Z. Zhang, C. Gao, and J. Luedtke, "New valid inequalities and formulation for the static chance-constrained lot-sizing problem", Mathematical Programming, 199:639-669 (2023). Preprint.
  5. R. Chen and J. Luedtke, "On generating Lagrangian Cuts for two-stage stochastic integer programs", INFORMS Journal on Computing, 34:2332-2349, (2022). Preprint.
  6. R. Chen and J. Luedtke, "On sample average approximation for two-stage stochastic programs without relatively complete recourse", Mathematical Programming, 196:719-754 (2022). Preprint.
  7. A. Smith, J. Linderoth, and J. Luedtke, "Optimization-based dispatching policies for open pit mining," Optimization and Engineering, 22:1347-1387 (2021) Preprint.
  8. C.H. Lim, J. Linderoth, J. Luedtke, and S. Wright, "Parallelizing subgradient methods for the Lagrangian dual in stochastic mixed-integer programming," INFORMS Journal on Optimization, 3:1-22 (2021). Preprint.
  9. A. Mahajan, S. Leyffer, J. Linderoth, J. Luedtke, and T. Munson, "Minotaur: A Mixed-Integer Nonlinear Optimization Toolkit," Mathematical Programming Computation, 13:301-338 (2021) Preprint.
  10. R. Kannan and J. Luedtke, "A stochastic approximation method for chance-constrained nonlinear programs," Mathematical Programming Computation, 13:705-751, (2021). Preprint.
  11. E. Towle and J. Luedtke, "Intersection disjunctions for reverse convex sets," Mathematics of Operations Research, 47:297-319, (2021). Preprint.
  12. A. Peña-Ordieres, J. Luedtke, and A. Waechter, "Solving chance-constrained problems via a smooth sample-based nonlinear approximation," SIAM Journal on Optimization, 30:2221-2250 (2020). Preprint.
  13. A. Soni, J. Linderoth, J. Luedtke, and F. Rigterink, "Mixed-integer linear programming for scheduling unconventional oil field development," Optimization and Engineering, (2020). Preprint.
  14. J. Luedtke, C. D'Ambrosio, J. Linderoth, and J. Schweiger, "Strong convex nonlinear relaxations of the pooling problem," SIAM Journal on Optimization, 30:1582-1609, (2020). Preprint.
  15. K. Zhang, L. Albert, J. Luedtke, and E. Towle, "A budgeted maximum multiple coverage model for cybersecurity planning and management," IISE Transactions, 51:1303-1317 (2019) Preprint.
  16. H. Ye, J. Luedtke, and H. Shen, "Call Center Arrivals: When to Jointly Forecast Multiple Streams?," Production and Operations Management, 28:27-42 (2019).
  17. M. Bodur and J. Luedtke, “Two-stage linear decision rules for multi-stage stochastic programming," Mathematical Programming, 191:347-380 (2022). Preprint (2018).
  18. E. Towle and J. Luedtke, "New solution approaches for the maximum-reliability stochastic network interdiction problem," Computational Management Science, 15:455-477 (2018). Preprint.
  19. M. Hamzeei and J. Luedtke, "Service network design with equilibrium-driven demands," IISE Transactions, 50:959-969 (2018).
  20. M. Bodur and J. Luedtke, “Integer programming formulations for minimum deficiency interval coloring", Networks, 72:249-271 (2018).
  21. A. Eberhard, N. Boland, J. Christiansen, B. Dandurand, J. Linderoth, J. Luedtke, and F. Oliveira, “Combining progressive hedging with a Frank-Wolfe method to compute Lagrangian dual bounds in stochastic mixed-integer programming,” SIAM Journal on Optimization, 28:1312-1336 (2018) .
  22. C.H. Lim, J. Linderoth, and J. Luedtke, “Valid inequalities for separable concave constraints with indicator variables,” Mathematical Programming, 172:415-442 (2018).
  23. T. Dinh, R. Fukasawa, and J. Luedtke, “Exact algorithms for the chance-constrained vehicle routing problem,” Mathematical Programming, 172:105-138 (2018).
  24. M. Kilinc, J. Linderoth, and J. Luedtke, “Lift-and-Project Cuts for Convex Mixed Integer Nonlinear Programs Linear Programming Based Separation and Extended Formulations,” Mathematical Programming Computation, 9:499-526 (2017). ( Preprint of earlier version)
  25. S. Ahmed, J. Luedtke, Y. Song, and W. Xie, “Nonanticipative duality, relaxations, and formulations for chance-constrained stochastic programs,” Mathematical Programming, 162:51-81 (2017).
  26. M. Bodur, S. Dash, O. Günlük, and J. Luedtke, “Strengthened Benders cuts for stochastic integer programs with continuous recourse,” INFORMS Journal on Computing, 29:77-91 (2017).
  27. M. Bodur and J. Luedtke, “Mixed-integer rounding enhanced benders decomposition for multiclass service system staffing and scheduling with arrival rate uncertainty,” Management Science, 63:2073-2091 (2017). Preprint.
  28. B. Kocuk, H. Jeon, S.S. Dey, J. Linderoth, J. Luedtke, and X. Sun, "A cycle-based formulation and valid inequalities for DC power transmission with switching,” Operations Research, 64(4):922-938, (2016).
  29. X. Liu, S. Küçükyavuz and J. Luedtke, “Decomposition algorithms for two-stage chance-constrained programs,” Mathematical Programming, 157:219-243 (2016).
  30. Y. Song and J. Luedtke, "An adaptive partition-based approach for solving two-stage stochastic programs with fixed recourse," SIAM Journal on Optimization, 25:1344-1367 (2015).
  31. M. Kilinc, J.T. Linderoth, J. Luedtke, A. Miller, “Strong branching inequalities for mixed integer nonlinear programs," Computational Optimization & Applications, 59:639-665 (2014).
  32. S. Sridhar, J. Linderoth, and J. Luedtke, "Models and solution techniques for production planning problems with increasing byproducts," Journal of Global Optimization, 59:597-631 (2014).
  33. M. Hamzeei and J. Luedtke, "Linearization-based algorithms for mixed-integer nonlinear programs with convex continuous relaxation," Journal of Global Optimization, 59:343-365 (2014).
  34. Y. Song, S. Kucukyavuz, and J. Luedtke, "Chance-constrained binary packing problems," INFORMS Journal on Computing, 26:735-747 (2014).
  35. J. Luedtke, "A branch-and-cut decomposition algorithm for solving chance-constrained mathematical programs with finite support," Mathematical Programming, 146:219-244 (2014).
  36. B. Armbruster and J. Luedtke, "Models and formulations for multivariate dominance constrained stochastic programs," IIE Transactions, 47:1-14 (2014).
  37. S. Sridhar, J. Linderoth, and J. Luedtke, "Locally ideal formulations for piecewise linear functions with indicator variables," Operations Research Letters, 41:627-632 (2013).
  38. Y. Song and J. Luedtke. “Branch-and-cut approaches for chance-constrained formulations of reliable network design problems,” Mathematical Programing Computation 5:397-432 (2013).
  39. P. Belotti, C. Kirches, S. Leyffer, J. Linderoth, J. Luedtke, and A. Mahajan, "Mixed-integer nonlinear optimization," Acta Numerica, 22:1-131 (2013).
  40. J. Luedtke, M. Namazifar, and J. Linderoth, “Some results on the strength of relaxations of multilinear functions,” Mathematical Programming, 136:325-351 (2012).
  41. I. Gurvich, J. Luedtke, and T. Tezcan, “Staffing call-centers with uncertain demand forecasts: a chance-constrained optimization approach,” Management Science, 56:1093-1115 (2010).
  42. J. Luedtke, S. Ahmed and G. Nemhauser, "An integer programming approach for linear programs with probabilistic constraints," Mathematical Programming, 122:247-272 (2010).
  43. J. Luedtke and G. Nemhauser, "Strategic planning with start-time dependent variable costs," Operations Research, 57:1250-1262 (2009).
  44. J. Luedtke, "New formulations for optimization under stochastic dominance constraints," SIAM Journal on Optimization, 19:1433-1450 (2008).
  45. J. Luedtke and S. Ahmed, "A sample approximation approach for optimization with probabilistic constraints," SIAM Journal on Optimization, 19:674-699 (2008). Preprint

Refereed Conference Papers:

  1. R. Chen and J. Luedtke, "Sparse multi-term disjunctive cuts for the epigraph of a function of binary variables", Integer Programming and Combinatorial Optimization (IPCO) 2022, Eindhoven, The Netherlands (2022). Preprint
  2. A. Soni, J. Linderoth, J. Luedtke, and D. Pimentel-Alarcon, "Integer programming approaches to subspace clustering with missing data", OPT2021: Optimization for Machine Learning, Spotlight talk, 2021.
  3. R. Kannan, J. Luedtke, and L. Roald, "Stochastic DC optimal power flow with reserve saturation", XXI Power Systems Control Conference (PSCC), Porto, Portugual (2020) Preprint.
  4. D. Coudert, J. Luedtke, E. Moreno, and K. Priftis, "Computing and maximizing the exact reliability of wireless backhaul networks," International Network Optimization Conference (INOC) 2017, Lisbon, Portugal (2017).
  5. T. Dinh, R. Fukasawa, and J. Luedtke, “Exact algorithms for the chance-constrained vehicle routing problem,” Integer Programming and Combinatorial Optimization (IPCO) 2016, Liege, Belgium (2016).
  6. C.H. Lim, J. Linderoth, and J. Luedtke, “Valid inequalities for separable concave constraints with indicator variables,” Integer Programming and Combinatorial Optimization (IPCO) 2016, Liege, Belgium (2016).
  7. C. D’Ambrosio, J. Linderoth, and J. Luedtke, “Valid inequalities for the pooling problem with binary variables,” 117-129, Integer Programming and Combinatorial Optimization (IPCO) 2011, New York, NY (2011).
  8. J. Luedtke, “An integer programming and decomposition approach to general chance-constrained mathematical programs,” Integer Programming and Combinatorial Optimization (IPCO) 2010, 271—284, Lausanne, Switzerland (2010).
  9. S. Leyffer, J. Linderoth, J. Luedtke, A. Miller and T. Munson, “Applications and algorithms for mixed integer nonlinear programming,” SciDAC 2009, J. of Physics: Conference Series, San Diego, California (2009).
  10. J. Luedtke, S. Ahmed and G. Nemhauser. "An integer programming approach for linear programs with probabilistic constraints," The Twelfth Conference for Integer Programming and Combinatorial Optimization (IPCO 2007), Proceedings. Lecture Notes in Computer Science 4513 (2007).
  11. J. Luedtke and C.C. White, III. "The value of asset visibility in the supply chain: single and dual source models," 2004 IEEE Conference on Systems, Man and Cybernetics: Proceedings, 5:4189-94 (2004).

Submitted Papers and Technical Reports:


Other Publications:


Slides:


Researh Group:

Current PhD students Former postdocs Former PhD students (primary advisor) Former PhD students (co-advisor)

Current Research Projects:


Completed Research Projects:


Professional Service:


Data and online supplements: