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ORCIDJan Kronqvist
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Journal Articles
- 2024
[j13]Vanya Aziz
, Ouyang Wu, Ivo Nowak, Eligius M. T. Hendrix, Jan Kronqvist:
On Optimizing Ensemble Models using Column Generation. J. Optim. Theory Appl. 203(2): 1794-1819 (2024)- 2023
- [j12]Alireza Olama, Eduardo Camponogara, Jan Kronqvist:
Sparse convex optimization toolkit: a mixed-integer framework. Optim. Methods Softw. 38(6): 1269-1295 (2023) - 2022
- [j11]Juan Javaloyes-Anton, Jan Kronqvist, José Antonio Caballero:
Simulation-based optimization of distillation processes using an extended cutting plane algorithm. Comput. Chem. Eng. 159: 107655 (2022) - [j10]Andreas Lundell, Jan Kronqvist:
Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT. J. Glob. Optim. 82(4): 863-896 (2022) - [j9]Andreas Lundell, Jan Kronqvist, Tapio Westerlund:
The supporting hyperplane optimization toolkit for convex MINLP. J. Glob. Optim. 84(1): 1-41 (2022) - [j8]David E. Bernal, Zedong Peng, Jan Kronqvist, Ignacio E. Grossmann:
Alternative regularizations for Outer-Approximation algorithms for convex MINLP. J. Glob. Optim. 84(4): 807-842 (2022) - 2021
- [j7]Alexander Thebelt, Jan Kronqvist, Miten Mistry, Robert M. Lee, Nathan Sudermann-Merx, Ruth Misener:
ENTMOOT: A framework for optimization over ensemble tree models. Comput. Chem. Eng. 151: 107343 (2021) - 2020
- [j6]Jan Kronqvist, David E. Bernal, Ignacio E. Grossmann:
Using regularization and second order information in outer approximation for convex MINLP. Math. Program. 180(1): 285-310 (2020) - 2019
- [j5]Jan Kronqvist, David E. Bernal, Andreas Lundell, Tapio Westerlund:
A center-cut algorithm for quickly obtaining feasible solutions and solving convex MINLP problems. Comput. Chem. Eng. 122: 105-113 (2019) - 2018
- [j4]Mikael Manngård, Jan Kronqvist, Jari M. Böling:
Structural learning in artificial neural networks using sparse optimization. Neurocomputing 272: 660-667 (2018) - [j3]Jan Kronqvist, Andreas Lundell, Tapio Westerlund:
Reformulations for utilizing separability when solving convex MINLP problems. J. Glob. Optim. 71(3): 571-592 (2018) - 2017
- [j2]Ville-Pekka Eronen, Jan Kronqvist, Tapio Westerlund, Marko M. Mäkelä, Napsu Karmitsa:
Method for solving generalized convex nonsmooth mixed-integer nonlinear programming problems. J. Glob. Optim. 69(2): 443-459 (2017) - 2016
- [j1]Jan Kronqvist, Andreas Lundell, Tapio Westerlund:
The extended supporting hyperplane algorithm for convex mixed-integer nonlinear programming. J. Glob. Optim. 64(2): 249-272 (2016)
Conference and Workshop Papers
- 2023
- [c7]Shudian Zhao, Calvin Tsay, Jan Kronqvist:
Model-Based Feature Selection for Neural Networks: A Mixed-Integer Programming Approach. LION 2023: 223-238 - [c6]Jan Kronqvist, Boda Li, Jan Rolfes, Shudian Zhao:
Alternating Mixed-Integer Programming and Neural Network Training for Approximating Stochastic Two-Stage Problems. LOD (2) 2023: 124-139 - [c5]Martin Ryner, Jan Kronqvist, Johan Karlsson:
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces. NeurIPS 2023 - 2021
- [c4]Jan Kronqvist, Ruth Misener, Calvin Tsay:
Between Steps: Intermediate Relaxations Between Big-M and Convex Hull Formulations. CPAIOR 2021: 299-314 - [c3]Calvin Tsay, Jan Kronqvist, Alexander Thebelt, Ruth Misener:
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks. NeurIPS 2021: 3068-3080 - 2020
- [c2]Elena Botoeva, Panagiotis Kouvaros, Jan Kronqvist, Alessio Lomuscio, Ruth Misener:
Efficient Verification of ReLU-Based Neural Networks via Dependency Analysis. AAAI 2020: 3291-3299 - 2019
- [c1]Andreas Lundell, Jan Kronqvist:
On Solving Nonconvex MINLP Problems with SHOT. WCGO 2019: 448-457
Informal and Other Publications
- 2024
- [i10]Martin Ryner, Jan Kronqvist, Johan Karlsson:
A cutting plane algorithm for globally solving low dimensional k-means clustering problems. CoRR abs/2402.13595 (2024) - [i9]Alireza Olama, Andreas Lundell, Jan Kronqvist, Elham Ahmadi, Eduardo Camponogara:
A GPU-Accelerated Bi-linear ADMM Algorithm for Distributed Sparse Machine Learning. CoRR abs/2405.16267 (2024) - [i8]Shudian Zhao, Jan Kronqvist:
A constrained optimization approach to improve robustness of neural networks. CoRR abs/2409.13770 (2024) - 2023
- [i7]Shudian Zhao, Calvin Tsay, Jan Kronqvist:
Model-based feature selection for neural networks: A mixed-integer programming approach. CoRR abs/2302.10344 (2023) - [i6]Martin Ryner, Jan Kronqvist, Johan Karlsson:
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces. CoRR abs/2307.09057 (2023) - 2022
- [i5]Alexander Thebelt, Johannes Wiebe, Jan Kronqvist, Calvin Tsay, Ruth Misener:
Maximizing information from chemical engineering data sets: Applications to machine learning. CoRR abs/2201.10035 (2022) - [i4]Jan Kronqvist, Ruth Misener, Calvin Tsay:
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints. CoRR abs/2202.05198 (2022) - 2021
- [i3]Jan Kronqvist, Ruth Misener, Calvin Tsay:
Between steps: Intermediate relaxations between big-M and convex hull formulations. CoRR abs/2101.12708 (2021) - [i2]Calvin Tsay, Jan Kronqvist, Alexander Thebelt, Ruth Misener:
Partition-based formulations for mixed-integer optimization of trained ReLU neural networks. CoRR abs/2102.04373 (2021) - 2020
- [i1]Alexander Thebelt, Jan Kronqvist, Miten Mistry, Robert M. Lee, Nathan Sudermann-Merx, Ruth Misener:
ENTMOOT: A Framework for Optimization over Ensemble Tree Models. CoRR abs/2003.04774 (2020)
Coauthor Index
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