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Publications

Published papers:

Journal papers:

  1. Chen, H., Kronqvist, J., and Ma, Z., 2025. A choice-based optimization approach for service operations in multimodal mobility systems. Transportation Research Part C: Emerging Technologies, 171, https://doi.org/10.1016/j.trc.2024.104954 
  2. Papageorgiou, D. J., Kronqvist, J., Ramanujam, A., Kor, J., Kim, Y., & Li, C. 2024. Solution polishing via path relinking for continuous black-box optimization. Optimization Letters, 1-42.
  3. Aziz, V., Wu, O., Nowak, I.,  Hendrix E. M. T., and Kronqvist J., 2024. On Optimizing Ensemble Models using Column Generation. Journal of Optimization Theory and Aplpications https://doi.org/10.1007/s10957-024-02391-9
  4. Papageorgiou, D.J., Kronqvist, J. and Kumaran, K., 2024. Linewalker: line search for black box derivative-free optimization and surrogate model construction. Optimization and Engineering, pp.1-65.
  5. Kronqvist, J., Li, B. and Rolfes, J, 2023. A mixed-integer approximation of robust optimization problems with mixed-integer adjustments. Optimization and Engineering, https://doi.org/10.1007/s11081-023-09843-7
  6. Olama, A., Camponogara, E. and Kronqvist, J., 2023. Sparse convex optimization toolkit: a mixed-integer framework. Optimization Methods and Software, 38(6), pp.1269-1295.
  7. Bernal, D.E., Peng, Z., Kronqvist, J. and Grossmann, I.E., 2022. Alternative regularizations for Outer-Approximation algorithms for convex MINLP. Journal of Global Optimization, 84(4), pp.807-842.
  8. Javaloyes-Anton, J., Kronqvist, J. and Caballero, J.A., 2022. Simulation-based optimization of distillation processes using an extended cutting plane algorithm. Computers & Chemical Engineering, 159, p.107655.
  9. Lundell, A., Kronqvist, J. and Westerlund, T., 2022. The supporting hyperplane optimization toolkit for convex MINLP. Journal of Global Optimization, 84(1), pp.1-41.
  10. Lundell, A. and Kronqvist, J., 2022. Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT. Journal of Global Optimization, 82(4), pp.863-896.
  11. Thebelt, A., Wiebe, J., Kronqvist, J., Tsay, C. and Misener, R., 2022. Maximizing information from chemical engineering data sets: Applications to machine learning. Chemical Engineering Science, 252, p.117469.
  12. Kronqvist, J. and Misener, R., 2021. A disjunctive cut strengthening technique for convex MINLP. Optimization and Engineering, 22(3), pp.1315-1345. Awarded best paper.
  13. Thebelt, A., Kronqvist, J., Mistry, M., Lee, R.M., Sudermann-Merx, N. and Misener, R., 2021. ENTMOOT: a framework for optimization over ensemble tree models. Computers & Chemical Engineering, 151, p.107343.
  14. Kronqvist, J., Bernal, D.E. and Grossmann, I.E., 2020. Using regularization and second order information in outer approximation for convex MINLP. Mathematical Programming, 180(1-2), pp.285-310.
  15. Kronqvist, J., Bernal, D.E., Lundell, A. and Grossmann, I.E., 2019. A review and comparison of solvers for convex MINLP. Optimization and Engineering, 20, pp.397-455.
  16. Kronqvist, J., Bernal, D.E., Lundell, A. and Westerlund, T., 2019. A center-cut algorithm for quickly obtaining feasible solutions and solving convex MINLP problems. Computers & Chemical Engineering, 122, pp.105-113.
  17. Kronqvist, J., Lundell, A. and Westerlund, T., 2018. Reformulations for utilizing separability when solving convex MINLP problems. Journal of Global Optimization, 71, pp.571-592.
  18. 16.   Manngård, M., Kronqvist, J. and Böling, J.M., 2018. Structural learning in artificial neural networks using sparse optimization. Neurocomputing, 272, pp.660-667.
  19. Eronen, V.P., Kronqvist, J., Westerlund, T., Mäkelä, M.M. and Karmitsa, N., 2017. Method for solving generalized convex nonsmooth mixed-integer nonlinear programming problems. Journal of Global Optimization, 69, pp.443-459.
  20. Kronqvist, J., Lundell, A. and Westerlund, T., 2016. The extended supporting hyperplane algorithm for convex mixed-integer nonlinear programming. Journal of Global Optimization, 64, pp.249-272.

 

Peer-reviewed conference proceedings

  1. Manngård, M., Bouzoulas, D., Hakonen, U., Viitala R., and Kronqvist, J., 2025. On the Real-Time Compliance of Moving-Horizon Simultaneous Input-and-State Estimation Problems. Accepted for publication in the Proceedings of American Control Conference (ACC) 2025.
  2. Ryner, M., Kronqvist, J. and Karlsson, J., 2024. Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces. Advances in Neural Information Processing Systems (NeurIPS), 36
  3. Kronqvist, J., Li, B., Rolfes, J., Zhao, S., 2024. Alternating Mixed-Integer Programming and Neural Network Training for Approximating Stochastic Two-Stage Problems. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14506. Springer, Cham.
  4. Zhao, S., Tsay, C., Kronqvist, J., 2023. Model-Based Feature Selection for Neural Networks: A Mixed-Integer Programming Approach. In: Sellmann, M., Tierney, K. (eds) Learning and Intelligent Optimization. LION 2023. Lecture Notes in Computer Science, vol 14286. Springer, Cham.
  5. Tsay, C., Kronqvist, J., Thebelt, A. and Misener, R., 2021. Partition-based formulations for mixed-integer optimization of trained ReLU neural networks. Advances in Neural Information Processing Systems (NeurIPS), 34, pp.3068-3080.
  6. Kronqvist, J., Misener, R. and Tsay, C., 2021. Between steps: Intermediate relaxations between big-M and convex hull formulations. In International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research (pp. 299-314). Cham: Springer International Publishing. Awarded best paper
  7. Botoeva, E., Kouvaros, P., Kronqvist, J., Lomuscio, A. and Misener, R., 2020. Efficient verification of relu-based neural networks via dependency analysis. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 3291-3299). (138 citations, Google Scholar)
  8. Thebelt, A., Kronqvist, J., Lee, R.M., Sudermann-Merx, N. and Misener, R., 2020. Global optimization with ensemble machine learning models. In Computer Aided Chemical Engineering (Vol. 48, pp. 1981-1986). Elsevier.
  9. Lundell, A., Kronqvist, J., 2020. On Solving Nonconvex MINLP Problems with SHOT. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham.
  10. Kronqvist, J. and Lundell, A., 2019. Convex MINLP–An Efficient Tool for Design and Optimization Tasks?. In Computer Aided Chemical Engineering (Vol. 47, pp. 245-250). Elsevier.
  11. Javaloyes-Antón, J., Kronqvist, J. and Caballero, J.A., 2018. Simulation-based optimization of chemical processes using the extended cutting plane algorithm. In Computer Aided Chemical Engineering (Vol. 43, pp. 463-469). Elsevier.
  12. Lundell, A., Kronqvist, J. and Westerlund, T., 2017. SHOT–A global solver for convex MINLP in Wolfram Mathematica. In Computer aided chemical engineering (Vol. 40, pp. 2137-2142). Elsevier.
  13. Kronqvist, J., Lundell, A. and Westerlund, T., 2017. A center-cut algorithm for solving convex mixed-integer nonlinear programming problems. In Computer Aided Chemical Engineering (Vol. 40, pp. 2131-2136). Elsevier.

 

PhD Thesis (Award for best PhD thesis at the Faculty of Science and Engineering)

  • Title: Polyhedral Outer Approximations in Convex Mixed-Integer Nonlinear Programming.  PhD advisor Tapio Westerlund.Link to PhD thesis
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