[1]
P. Rytı́ř, A. Wodecki, G. Korpas, and J. Marecek, “ExDBN: Learning dynamic bayesian networks using extended mixed-integer programming formulations,”
Transactions on Machine Learning Research, 2025, [Online]. Available:
https://openreview.net/forum?id=I64MJzl9Fy
[2]
P. Rytir, A. Wodecki, and J. Marecek, “ExDAG: an MIQP Algorithm for Learning DAGs,” Nov. 19, 2025,
arXiv: arXiv:2406.15229. doi:
10.48550/arXiv.2406.15229.
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P. Rytir
et al., “Power System Steady-State Estimation Revisited,” Jan. 06, 2025,
arXiv: arXiv:2501.03400. doi:
10.48550/arXiv.2501.03400.
[4]
P. Rytir, P. C. Burke, C. Aravanis, J. Vala, and J. Marecek, “Topological Quantum Compilation Using Mixed-Integer Programming,” Nov. 12, 2025,
arXiv: arXiv:2511.09513. doi:
10.48550/arXiv.2511.09513.
[5]
P. Ryšavý, P. Rytíř, X. He, G. Korpas, and J. Mareček, “ExMAG: Learning of Maximally Ancestral Graphs,” May 22, 2025,
arXiv: arXiv:2503.08245. doi:
10.48550/arXiv.2503.08245.
[6]
V. Kungurtsev, F. Idlahcen, P. Rysavy, P. Rytir, and A. Wodecki, “Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons.” 2024. [Online]. Available:
https://arxiv.org/abs/2406.17585
[7]
A. Wodecki, P. Rytir, V. Kungurtsev, and J. Marecek, “Scheduling a Multi-Product Pipeline: A Discretized MILP Formulation,” Dec. 18, 2023,
arXiv: arXiv:2312.11381. doi:
10.48550/arXiv.2312.11381.
[8]
R. Horčík, Á. Torralba, P. Rytíř, L. Chrpa, and S. Edelkamp, “Optimal Mixed Strategies for Cost-Adversarial Planning Games,”
Proceedings of the International Conference on Automated Planning and Scheduling, vol. 32, pp. 160–168, June 2022, doi:
10.1609/icaps.v32i1.19797.
[9]
L. Chrpa, P. Rytı́r, A. Nyporko, R. Horcı́k, and S. Edelkamp, “Effective planning in resource-competition problems by task decomposition,” in
Proceedings of the fifteenth international symposium on combinatorial search, SOCS 2022, vienna, austria, july 21-23, 2022, L. Chrpa and A. Saetti, Eds., AAAI Press, 2022, pp. 47–55. [Online]. Available:
https://ojs.aaai.org/index.php/SOCS/article/view/21751
[10]
L. Chrpa, P. Rytı́r, R. Horcı́k, and S. Edelkamp, “Competing for resources: Estimating adversary strategy for effective plan generation,” in
Thirty-sixth AAAI conference on artificial intelligence, AAAI 2022, thirty-fourth conference on innovative applications of artificial intelligence, IAAI 2022, the twelveth symposium on educational advances in artificial intelligence, EAAI 2022 virtual event, february 22 - march 1, 2022, AAAI Press, 2022, pp. 9707–9715. [Online]. Available:
https://ojs.aaai.org/index.php/AAAI/article/view/21205
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L. Chrpa, P. Rytı́ř, and R. Horčı́k, “Planning against adversary in zero-sum games: Heuristics for selecting and ordering critical actions,” in Proceedings of the thirteenth international symposium on combinatorial search, SOCS 2020, online conference [vienna, austria], 26-28 may 2020, 2020, pp. 20–28.
[12]
P. Rytíř, L. Chrpa, and B. Bošanský, “Using Classical Planning in Adversarial Problems,” in
2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Nov. 2019, pp. 1335–1340. doi:
10.1109/ICTAI.2019.00185.
[13]
M. Loebl and P. Rytíř, “Binary linear codes, dimers and hypermatrices,”
Electronic Notes in Discrete Mathematics, vol. 59, pp. 19–35, June 2017, doi:
10.1016/j.endm.2017.05.003.
[14]
P. Rytíř, “Geometric representations of linear codes,”
Advances in Mathematics, vol. 282, pp. 1–22, Sept. 2015, doi:
10.1016/j.aim.2015.06.011.
[15]
P. Rytı́ř, “Geometric and algebraic properties of discrete structures,” phd, Faculty of Mathematics and Physics, Charles University in Prague, http://kam.mff.cuni.cz/ rytir/papers/rytirphdthesis2.pdf, 2013.
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P. Rytíř, “Geometric representations of binary codes and computation of weight enumerators,”
Advances in Applied Mathematics, vol. 45, no. 2, pp. 290–301, Aug. 2010, doi:
10.1016/j.aam.2009.12.001.
[17]
P. Rytı́ř, “Lattices and codes,” master, Faculty of Mathematics and Physics, Charles University in Prague, 2007.