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Documents authored by Sidorov, Konstantin

Document
DOI: 10.4230/LIPIcs.CP.2025.35
Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems

Authors: Konstantin Sidorov, Imko Marijnissen, and Emir Demirović

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Constraint programming solvers have seen much success in scheduling problems owing to their efficient reasoning over constraints to solve complex problems in practice. Many algorithms have been proposed for propagating information from a single constraint. However, inferring and exchanging information across multiple constraints can provide deeper insight into the global structure of a problem. In this work, we propose to exchange information amongst constraints by inferring the disjointness of tasks in scheduling problems from many constraints. We do this by (i) augmenting existing propagators, such as the Cumulative and nogoods, to report when pairs of tasks are disjoint, and (ii) leveraging this information by introducing the SelectiveDisjunctive propagator which generates a lower bound on the earliest completion time of cliques of disjoint tasks to determine conflicts. This allows us to aggregate disjointness information spanning multiple constraints to gain a better global overview of the problem, as well as more precise local information. We also identify a problem structure where an LCG solver reasoning over Cumulative constraints separately, without any reformulations, requires an exponential amount of time to prove infeasibility, which we both justify theoretically and show empirically; on the other hand, our approach solves those instances in polynomial time. On particular known RCPSP and RCPSP/max benchmarks, our approach significantly reduces the number of conflicts required to prove optimality when resource contention is high. Additionally, we discover new lower bounds for 16 RCPSP/max instances (closing six of them) and four RCPSP instances (closing one), as well as new upper bounds for two RCPSP/max instances and four RCPSP instances. Furthermore, we empirically analyse our proposed approach to determine which features are beneficial for performance, showing that finding cliques is one of the main bottlenecks and that detecting disjointness during search can lead to improved bounds on certain instances, but it generally negatively impacts learning. This work paves the way for reasoning over the disjointness of tasks inferred from a variety of standard constraints to discover novel information sourced from multiple constraints during search.
Cite as

Konstantin Sidorov, Imko Marijnissen, and Emir Demirović. Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 35:1-35:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{sidorov_et_al:LIPIcs.CP.2025.35,
  author =	{Sidorov, Konstantin and Marijnissen, Imko and Demirovi\'{c}, Emir},
  title =	{{Unite and Lead: Finding Disjunctive Cliques for Scheduling Problems}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{35:1--35:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.35},
  URN =		{urn:nbn:de:0030-drops-238969},
  doi =		{10.4230/LIPIcs.CP.2025.35},
  annote =	{Keywords: Constraint Programming, Lazy Clause Generation, Propagation, Scheduling, Cumulative, Disjunctive}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.9
Pseudo-Boolean Reasoning About States and Transitions to Certify Dynamic Programming and Decision Diagram Algorithms

Authors: Emir Demirović, Ciaran McCreesh, Matthew J. McIlree, Jakob Nordström, Andy Oertel, and Konstantin Sidorov

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Pseudo-Boolean proof logging has been used successfully to provide certificates of optimality from a variety of constraint- and satisifability-style solvers that combine reasoning with a backtracking or clause-learning search. Another paradigm, occurring in dynamic programming and decision diagram solving, instead reasons about partial states and possible transitions between them. We describe a framework for generating clean and efficient pseudo-Boolean proofs for these kinds of algorithm, and use it to produce certifying algorithms for knapsack, longest path, and interval scheduling. Because we use a common proof system, we can also reason about hybrid solving algorithms: we demonstrate this by providing proof logging for a dynamic programming based knapsack propagator inside a constraint programming solver.
Cite as

Emir Demirović, Ciaran McCreesh, Matthew J. McIlree, Jakob Nordström, Andy Oertel, and Konstantin Sidorov. Pseudo-Boolean Reasoning About States and Transitions to Certify Dynamic Programming and Decision Diagram Algorithms. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 9:1-9:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{demirovic_et_al:LIPIcs.CP.2024.9,
  author =	{Demirovi\'{c}, Emir and McCreesh, Ciaran and McIlree, Matthew J. and Nordstr\"{o}m, Jakob and Oertel, Andy and Sidorov, Konstantin},
  title =	{{Pseudo-Boolean Reasoning About States and Transitions to Certify Dynamic Programming and Decision Diagram Algorithms}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{9:1--9:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.9},
  URN =		{urn:nbn:de:0030-drops-206948},
  doi =		{10.4230/LIPIcs.CP.2024.9},
  annote =	{Keywords: Proof logging, dynamic programming, decision diagrams}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.11
A Multi-Stage Proof Logging Framework to Certify the Correctness of CP Solvers

Authors: Maarten Flippo, Konstantin Sidorov, Imko Marijnissen, Jeff Smits, and Emir Demirović

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Proof logging is used to increase trust in the optimality and unsatisfiability claims of solvers. However, to this date, no constraint programming solver can practically produce proofs without significantly impacting performance, which hinders mainstream adoption. We address this issue by introducing a novel proof generation framework, together with a CP proof format and proof checker. Our approach is to divide the proof generation into three steps. At runtime, we require the CP solver to only produce a proof sketch, which we call a scaffold. After the solving is done, our proof processor trims and expands the scaffold into a full CP proof, which is subsequently verified. Our framework is agnostic to the solver and the verification approach. Through MiniZinc benchmarks, we demonstrate that with our framework, the overhead of logging during solving is often less than 10%, significantly lower than other approaches, and that our proof processing step can reduce the overall size of the proof by orders of magnitude and by extension the proof checking time. Our results demonstrate that proof logging has the potential to become an integral part of the CP community.
Cite as

Maarten Flippo, Konstantin Sidorov, Imko Marijnissen, Jeff Smits, and Emir Demirović. A Multi-Stage Proof Logging Framework to Certify the Correctness of CP Solvers. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{flippo_et_al:LIPIcs.CP.2024.11,
  author =	{Flippo, Maarten and Sidorov, Konstantin and Marijnissen, Imko and Smits, Jeff and Demirovi\'{c}, Emir},
  title =	{{A Multi-Stage Proof Logging Framework to Certify the Correctness of CP Solvers}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{11:1--11:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.11},
  URN =		{urn:nbn:de:0030-drops-206969},
  doi =		{10.4230/LIPIcs.CP.2024.11},
  annote =	{Keywords: proof logging, formal verification, constraint programming}
}
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