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Optimization for and with Machine Learning (OPTIMAL) OPTIMAL

Preprints and publications

Dissemination from the research project Optimization for and with Machine Learning (OPTIMAL) is currently forthcoming. Preprints and publications are shared on this project website and offer more information about the objective of the research project and meet the valorisation goals.

Preprints and publications 2023

  • Karen Aardal, Lara Scavuzzo, and Laurence A. Wolsey (2023). A study of lattice reformulations for integer programming, Operations Research Letters, Vol. 51 (4): 401-407. https://doi.org/10.1016/j.orl.2023.05.001
  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani (2023). Convergence rate analysis of randomized and cyclic coordinate descent for convex optimization through semidefinite programming, Applied Set-Valued Analysis and Optimization, Vol 5 (2), 141-153. https://doi.org/10.23952/asvao.5.2023.2.02
  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani (2023). Conditions for linear convergence of the gradient method for non-convex optimization, Optimization Letters 17, 1105–1125. https://doi.org/10.1007/s11590-023-01981-2
  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani (2023). On the Rate of Convergence of the Difference-of-Convex Algorithm (DCA), Journal of Optimization Theory and Applications, 1-22.  https://doi.org/10.1007/s10957-023-02199-z
  • Giulia Bernardini, Alessio Conte, Garance Gourdel, Roberto Grossi, Grigorios Loukides, Nadia Pisanti, Solon Pissis, Giulia Punzi, Leen Stougie, Michelle Sweering (2023). Hide and Mine in Strings: Hardness, Algorithms, and Experiments, IEEE Transactions on Knowledge and Data Engineering, Vol 35 (6), 5948 – 5963. https://doi.org/10.1109/TKDE.2022.3158063
  • Giulia Bernardini, Leo van Iersel, Esther Julien, Leen Stougie (2023, accepted for publication). Constructing Phylogenetic Networks via Cherry Picking and Machine Learning. Algorithms for Molecular Biology.
  • Danish Kashaev, Guido Schäfer (2023, forthcoming). Round and Bipartize for Vertex Cover Approximation, LIPIcs, APROX/RANDOM. Vol. 275. https://approxconference.wordpress.com
  • Alexander Taveira Blomenhofer (2023). Gaussian Mixture Identifiability from degree 6 Moments, Preprint at arXiv: 2307.03850. https://doi.org/10.48550/arXiv.2307.03850
  • Alexander Taveira Blomenhofer (2023). Unique Powers-of-Forms Decompositions from Simple Gram Spectrahedra, Preprint at arXiv: 2305.06860. http://arxiv.org/abs/2305.06860
  • Lijun Ding, Alex L. Wang (2023). Sharpness and well-conditioning of nonsmooth convex formulations in statistical signal recovery, Preprint at arXiv: 2307.06873. https://arxiv.org/abs/2307.06873
  • Adejuyigbe Fajemisin, Donato Maragno, Dick den Hertog (2023, in press). Optimization with constraint learning: a framework and survey, European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2023.04.041
  • Mayukh Ghosh, Alex Kuiper,  Roshan Mahes, & Donato Maragno (2023). Learn global and optimize local: A data-driven methodology for last-mile routing, Computers & Operations Research, Vol. 159, 106312. https://doi.org/10.1016/j.cor.2023.106312
  • Donato Maragno, Jannis Kurtz,  Tabea Röber, Rob Goedhart, S. Ilker Birbil & Dick den Hertog (2023). Finding regions of counterfactual explanations via robust optimization, Preprint at arXiv: 2301.11113. https://doi.org/10.48550/arXiv.2301.11113
  • Akshay Ramachandran, Kevin Shu, Alex Wang (2023). Hidden convexity, optimization, and algorithms on rotation matrices, Preprint at arXiv: 2304.08596. https://doi.org/10.48550/arXiv.2304.08596

2022

  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani. (2022). Convergence rate analysis of randomized and cyclic coordinate descent for convex optimization through semidefinite programming. Preprint at arXiv: 2212.12384. https://arxiv.org/abs/2212.12384.
  • Giulia Bernardini, Huiping Chen, Grigorios Loukides, Solon P. Pissis, Leen Stougie, Michelle Sweering (2022). Making de Bruijn Graphs Eulerian, CPM 2022: 12:1-12:18. https://doi.org/10.4230/LIPIcs.CPM.2022.12
  • Giulia Bernardini, Alessio Conte, Estéban Gabory, Roberto Grossi, Grigorios Loukides, Solon P. Pissis, Giulia Punzi, Michelle Sweering (2022). On Strings Having the Same Length- k Substrings, CPM 2022: 16:1-16:17. https://doi.org/10.4230/LIPIcs.CPM.2022.16
  • Giulia Bernardini, Alessio Conte, Garance Gourdel, Roberto Grossi, Grigorios Loukides, Nadia Pisanti, Solon Pissis, Giulia Punzi, Leen Stougie, Michelle Sweering (2022). Hide and Mine in Strings: Hardness, Algorithms, and Experiments, IEEE Transactions on Knowledge and Data Engineering. https://ieeexplore.ieee.org/document/9732522.
  • Giulia Bernardini, Estéban Gabory, Solon P Pissis, Leen Stougie, Michelle Sweering, Wiktor Zuba (2022). Elastic-Degenerate String Matching with 1 Error, Preprint at arXiv: 2209.01095. https://arxiv.org/abs/2209.01095.
  • Giulia Bernardini, Pawel Gawrychowski, Nadia Pisanti, Solon P. Pissis, Giovanna Rosone (2022). Elastic-Degenerate String Matching via Fast Matrix Multiplication, LATIN 2022: Theoretical Informatics; 20-37. Conference publication. https://doi.org/10.1007/978-3-031-20624-5_2
  • Giulia Bernardini, Pawel Gawrychowski, Nadia Pisanti, Solon P. Pissis, Giovanna Rosone (2022). Elastic-Degenerate String Matching via Fast Matrix Multiplication, SIAM J. Comput. 51(3): 549-576. 
    https://doi.org/10.1137/20M1368033
  • Giulia Bernardini, Leo van Iersel, Esther Julien, Leen Stougie (2022). Reconstructing Phylogenetic Networks via Cherry Picking and Machine Learning, WABI 2022. https://doi.org/10.0.16.134/LIPIcs.WABI.2022.16
  • Giulia Bernardini, Alexander Lindermayr, Alberto Marchetti-Spaccamela, Nicole Megow, Leen Stougie, Michelle Sweering (2022). A Universal Error Measure for Input Predictions Applied to Online Graph Problems, NeurIPS 2022 Conference. https://openreview.net/forum?id=a7-YO5NJGyp
  • Giulia Bernardini, Alexander Lindermayr, Alberto Marchetti-Spaccamela, Nicole Megow, Leen Stougie, Michelle Sweering (2022). A Universal Error Measure for Input Predictions Applied to Online Graph Problems, Preprint at arXiv: 2205.12850. https://arxiv.org/abs/2205.12850.
  • Thomas Bosman, Martijn van Ee, Yang Jiao, Alberto Marchetti-Spaccamela, R. Ravi, Leen Stougie (2022). Approximation Algorithms for Replenishment Problems with Fixed Turnover Times, Algorithmica 84(9): 2597-2621. https://doi.org/10.1007/s00453-022-00974-4
  • Esther Julien, Krzysztof Postek, Ş. İlker Birbil (2022). Machine Learning for $K$-adaptability in Two-stage Robust Optimization, Preprint at arXiv: 2210.11152. https://arxiv.org/abs/2210.11152
  • Danish Kashaev, Guido Schäfer (2022). Round and Bipartize for Vertex Cover Approximation. Preprint at Archiv: 2211.01699. https://arxiv.org/abs/2211.01699
  • Donato Maragno, Tabea Röber & S. Ilker Birbil (2022). Counterfactual explanations using optimization with constraint learning, Preprint at arXiv: 2209.10997. https://doi.org/10.48550/arXiv.2209.10997
  • Lara Scavuzzo, Feng Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, and Karen Aardal (2022). Learning to branch with tree mdps, Advances in Neural Information Processing Systems 35: 18514-18526.
  • Moslem Zamani, Hadi Abbaszadehpeivasti, Etienne de Klerk (2022). Convergence rate analysis of the gradient descent-ascent method for convex-concave saddle-point problems, Preprint at arXiv: 2209.01272. https://arxiv.org/abs/2209.01272

2021

  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani. (2021). The exact worst-case convergence rate of the gradient method with fixed step lengths for L-smooth functions, Optimization Letters, 16(6), 1649-1661. https://doi.org/10.1007/s11590-021-01821-1
  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani (April 2021). The Exact Worst-Case Convergence Rate of the Gradient Method with Fixed Step Lengths for L-Smooth Functions, Preprint arXiv:2104.05468 [math.OC]. https://arxiv.org/abs/2104.05468
  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani (September 2021). On the rate of convergence of the Difference-of-Convex Algorithm (DCA), Preprint arXiv: 2109.13566 [math.OC]. https://arxiv.org/abs/2109.13566
  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani (April 2022). Conditions for Linear Convergence of the Gradient Method for Non-Convex Optimization, Preprint arXiv: 2204.00647 [math.OC]. https://arxiv.org/abs/2204.00647
  • Hadi Abbaszadehpeivasti, Etienne de Klerk, Moslem Zamani (June 2022). The exact worst-case convergence rate of the alternating direction method of multipliers, Preprint arXiv: 2206.09865 [math.OC]. https://arxiv.org/abs/2206.09865v1
  • Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak, Bertrand Simon October (2021). Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds, Preprint arXiv:2110.13116 [cs.DS]. https://arxiv.org/abs/2110.13116#
  • Antonios Antoniadis, Christian Coester, Marek Eliáš, Adam Polak, Bertrand Simon October (2021). Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds, Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 35th Conference on Neural Information Processing Systems (NeurIPS 2021). https://papers.nips.cc/paper_files/paper/2021/hash/8b8388180314a337c9aa3c5aa8e2f37a-Abstract.html​​​​​​​
  • Giulia Bernardini, Paola Bonizzoni, Pawel Gawrychowski (2021). Incomplete Directed Perfect Phylogeny in Linear Time, WADS 2021: 172-185. https://doi.org/10.1007/978-3-030-83508-8_13
  • Giulia Bernardini, Alberto Marchetti-Spaccamela, Solon P. Pissis, Leen Stougie, Michelle Sweering (2021). Constructing Strings Avoiding Forbidden Substrings, CPM 2021: 9:1-9:18. https://doi.org/0.4230/LIPIcs.CPM.2021.9
  • Antje Bjelde, Jan Hackfeld, Yann Disser, Christoph Hansknecht, Maarten Lipmann, Julie Meißner, Miriam Schlöter, Kevin Schewior, Leen Stougie (2021). Tight Bounds for Online TSP on the Line, ACM Trans. Algorithms 17(1): 3:1-3:58. https://doi.org/10.1145/3422362
  • Mark Bun, Marek Elláš, Janardhan Kulkarni (2021). Differentially Private Correlation Clustering. Preprint arXiv:2102.08885. Accepted to ICML 021. https://arxiv.org/abs/2102.08885
  • Huiping Chen, Changyu Dong, Liyue Fan, Grigorios Loukides, Solon P. Pissis, Leen Stougie (2021). Differentially Private String Sanitization for Frequency-Based Mining Tasks, ICDM 2021: 41-50. https://ieeexplore.ieee.org/document/9679009
  • Lin Chen, Nicole Megow, Roman Rischke, Leen Stougie, José Verschae (2021). Optimal algorithms for scheduling under time-of-use tariffs, Ann. Oper. Res. 304(1): 85-107. https://doi.org/10.1007/s10479-021-04059-3
  • Koen M.J. De Bontridder, Bjarni V. Halldórsson, Magnús M. Halldórsson, Cor A. J. Hurkens, Jan Karel Lenstra, R. Ravi, Leen Stougie (2021). Local improvement algorithms for a path packing problem: A performance analysis based on linear programming, Oper. Res. Lett. 49(1): 62-68. https://doi.org/10.1016/j.orl.2020.11.005
  • Martin E. Dyer, Catherine S. Greenhill, Pieter Kleer, James Ross, Leen Stougie (2021). Sampling hypergraphs with given degrees, Discret. Math. 344(11): 112566. https://doi.org/10.1016/j.disc.2021.112566
  • Adejuyigbe Fajemisin, Donato Maragno, Dick den Hertog (October 2021). Optimization with Constraint Learning: A Framework and Survey, Preprint arXiv:2110.02121 [cs.LG]. https://arxiv.org/abs/2110.02121
  • Mark Jones, Steven Kelk, Leen Stougie (2021). Maximum parsimony distance on phylogenetic trees: A linear kernel and constant factor approximation algorithm, J. Comput. Syst. Sci. 117: 165-181. 
    https://doi.org/10.1016/j.jcss.2020.10.003
  • Donato Maragno, Holly Wiberg, Dimitris Bertsimas, S. Ilker Birbil, Dick den Hertog, Adejuyigbe Fajemisin (November 2021). Mixed-Integer Optimization with Constraint Learning, Preprint arXiv:2111.04469 [math.OC] https://arxiv.org/abs/2111.04469. OptiCL, Python package: https://github.com/hwiberg/OptiCL.
  • Takuya Mieno, Solon P. Pissis, Leen Stougie, Michelle Sweering (2021). String Sanitization Under Edit Distance: Improved and Generalized, CPM 2021: 19:1-19:18. http://doi.org/10.4230/LIPIcs.CPM.2021.19
  • Rosanne Wallin, Leo van Iersel, Steven Kelk, Leen Stougie (2021). Applicability of several rooted phylogenetic network algorithms for representing the evolutionary history of SARS-CoV-2, BMC Ecology and Evolution, 21:220. https://doi.org/10.1186/s12862-021-01946-y

2020

  • Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, and Andrea Lodi (2020). Ecole: A gym-like library for machine learning in combinatorial optimization solvers. Preprint at Archiv: 2011.06069. https://doi.org/10.48550/arXiv.2011.06069