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Documents authored by Manquinho, Vasco

Document
DOI: 10.4230/LIPIcs.CP.2024.8
Slide&Drill, a New Approach for Multi-Objective Combinatorial Optimization

Authors: João Cortes, Inês Lynce, and Vasco Manquinho

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

Abstract
Following the successful use of Propositional Satisfiability (SAT) algorithms in Boolean optimization (e.g., Maximum Satisfiability), several SAT-based algorithms have been proposed for Multi-Objective Combinatorial Optimization (MOCO). However, these new algorithms either provide a small subset of the Pareto front or follow a more exploratory search procedure and the solutions found are usually distant from the Pareto front. We extend the state of the art with a new SAT-based MOCO solver, Slide and Drill (Slide&Drill), that hones an upper bound set of the exact solution. Moreover, we show that Slide&Drill neatly complements proposed UNSAT-SAT algorithms for MOCO. These algorithms can work in tandem over the same shared "blackboard" formula, in order to enable a faster convergence. Experimental results in several sets of benchmark instances show that Slide&Drill can outperform other SAT-based algorithms for MOCO, in particular when paired with previously proposed UNSAT-SAT algorithms.
Cite as

João Cortes, Inês Lynce, and Vasco Manquinho. Slide&Drill, a New Approach for Multi-Objective Combinatorial Optimization. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{cortes_et_al:LIPIcs.CP.2024.8,
  author =	{Cortes, Jo\~{a}o and Lynce, In\^{e}s and Manquinho, Vasco},
  title =	{{Slide\&Drill, a New Approach for Multi-Objective Combinatorial Optimization}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{8:1--8:17},
  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.2024.8},
  URN =		{urn:nbn:de:0030-drops-206932},
  doi =		{10.4230/LIPIcs.CP.2024.8},
  annote =	{Keywords: Multi-Objective Combinatorial Optimization, Satisfiability Algorithms}
}
Document
DOI: 10.4230/LIPIcs.SAT.2023.19
UpMax: User Partitioning for MaxSAT

Authors: Pedro Orvalho, Vasco Manquinho, and Ruben Martins

Published in: LIPIcs, Volume 271, 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)

Abstract
It has been shown that Maximum Satisfiability (MaxSAT) problem instances can be effectively solved by partitioning the set of soft clauses into several disjoint sets. The partitioning methods can be based on clause weights (e.g., stratification) or based on graph representations of the formula. Afterwards, a merge procedure is applied to guarantee that an optimal solution is found. This paper proposes a new framework called UpMax that decouples the partitioning procedure from the MaxSAT solving algorithms. As a result, new partitioning procedures can be defined independently of the MaxSAT algorithm to be used. Moreover, this decoupling also allows users that build new MaxSAT formulas to propose partition schemes based on knowledge of the problem to be solved. We illustrate this approach using several problems and show that partitioning has a large impact on the performance of unsatisfiability-based MaxSAT algorithms.
Cite as

Pedro Orvalho, Vasco Manquinho, and Ruben Martins. UpMax: User Partitioning for MaxSAT. In 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 271, pp. 19:1-19:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{orvalho_et_al:LIPIcs.SAT.2023.19,
  author =	{Orvalho, Pedro and Manquinho, Vasco and Martins, Ruben},
  title =	{{UpMax: User Partitioning for MaxSAT}},
  booktitle =	{26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
  pages =	{19:1--19:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-286-0},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{271},
  editor =	{Mahajan, Meena and Slivovsky, Friedrich},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.19},
  URN =		{urn:nbn:de:0030-drops-184819},
  doi =		{10.4230/LIPIcs.SAT.2023.19},
  annote =	{Keywords: Maximum Satisfiability, Formula partitioning, Graph-based methods}
}
Document
DOI: 10.4230/LIPIcs.SAT.2022.29
SAT-Based Leximax Optimisation Algorithms

Authors: Miguel Cabral, Mikoláš Janota, and Vasco Manquinho

Published in: LIPIcs, Volume 236, 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)

Abstract
In several real-world problems, it is often the case that the goal is to optimise several objective functions. However, usually there is not a single optimal objective vector. Instead, there are many optimal objective vectors known as Pareto-optima. Finding all Pareto-optima is computationally expensive and the number of Pareto-optima can be too large for a user to analyse. A compromise can be made by defining an optimisation criterion that integrates all objective functions. In this paper we propose several SAT-based algorithms to solve multi-objective optimisation problems using the leximax criterion. The leximax criterion is used to obtain a Pareto-optimal solution with a small trade-off between the objective functions, which is suitable in problems where there is an absence of priorities between the objective functions. Experimental results on the Multi-Objective Package Upgradeability Optimisation problem show that the SAT-based algorithms are able to outperform the Integer Linear Programming (ILP) approach when using non-commercial ILP solvers. Additionally, experimental results on selected instances from the MaxSAT evaluation adapted to the multi-objective domain show that our approach outperforms the ILP approach using commercial solvers.
Cite as

Miguel Cabral, Mikoláš Janota, and Vasco Manquinho. SAT-Based Leximax Optimisation Algorithms. In 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 236, pp. 29:1-29:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{cabral_et_al:LIPIcs.SAT.2022.29,
  author =	{Cabral, Miguel and Janota, Mikol\'{a}\v{s} and Manquinho, Vasco},
  title =	{{SAT-Based Leximax Optimisation Algorithms}},
  booktitle =	{25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022)},
  pages =	{29:1--29:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-242-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{236},
  editor =	{Meel, Kuldeep S. and Strichman, Ofer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2022.29},
  URN =		{urn:nbn:de:0030-drops-167030},
  doi =		{10.4230/LIPIcs.SAT.2022.29},
  annote =	{Keywords: Multi-Objective Optimisation, Leximax, Sorting Networks}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.31
The Seesaw Algorithm: Function Optimization Using Implicit Hitting Sets

Authors: Mikoláš Janota, António Morgado, José Fragoso Santos, and Vasco Manquinho

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
The paper introduces the Seesaw algorithm, which explores the Pareto frontier of two given functions. The algorithm is complete and generalizes the well-known implicit hitting set paradigm. The first given function determines a cost of a hitting set and is optimized by an exact solver. The second, called the oracle function, is treated as a black-box. This approach is particularly useful in the optimization of functions that are impossible to encode into an exact solver. We show the effectiveness of the algorithm in the context of static solver portfolio selection. The existing implicit hitting set paradigm is applied to cost function and an oracle predicate. Hence, the Seesaw algorithm generalizes this by enabling the oracle to be a function. The paper identifies two independent preconditions that guarantee the correctness of the algorithm. This opens a number of avenues for future research into the possible instantiations of the algorithm, depending on the cost and oracle functions used.
Cite as

Mikoláš Janota, António Morgado, José Fragoso Santos, and Vasco Manquinho. The Seesaw Algorithm: Function Optimization Using Implicit Hitting Sets. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 31:1-31:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{janota_et_al:LIPIcs.CP.2021.31,
  author =	{Janota, Mikol\'{a}\v{s} and Morgado, Ant\'{o}nio and Fragoso Santos, Jos\'{e} and Manquinho, Vasco},
  title =	{{The Seesaw Algorithm: Function Optimization Using Implicit Hitting Sets}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{31:1--31:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.31},
  URN =		{urn:nbn:de:0030-drops-153220},
  doi =		{10.4230/LIPIcs.CP.2021.31},
  annote =	{Keywords: implicit hitting sets, minimal hitting set, MaxSAT, optimization}
}
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