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Software Architecture and Machine Learning (Dagstuhl Seminar 23302)

Authors: Grace A. Lewis, Henry Muccini, Ipek Ozkaya, Karthik Vaidhyanathan, Roland Weiss, and Liming Zhu

Published in: Dagstuhl Reports, Volume 13, Issue 7 (2024)

Abstract

This report documents the program and outcomes of Dagstuhl Seminar 23302, "Software Architecture and Machine Learning". We summarize the goals and format of the seminar, results from the breakout groups, key definitions relevant to machine learning-enabled systems that were discussed, and the research roadmap that emerged from the discussions during the seminar. The report also includes the abstracts of the talks presented at the seminar and summaries of open discussions.

Cite as

Grace A. Lewis, Henry Muccini, Ipek Ozkaya, Karthik Vaidhyanathan, Roland Weiss, and Liming Zhu. Software Architecture and Machine Learning (Dagstuhl Seminar 23302). In Dagstuhl Reports, Volume 13, Issue 7, pp. 166-188, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{lewis_et_al:DagRep.13.7.166,
  author =	{Lewis, Grace A. and Muccini, Henry and Ozkaya, Ipek and Vaidhyanathan, Karthik and Weiss, Roland and Zhu, Liming},
  title =	{{Software Architecture and Machine Learning (Dagstuhl Seminar 23302)}},
  pages =	{166--188},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{7},
  editor =	{Lewis, Grace A. and Muccini, Henry and Vaidhyanathan, Karthik and Weiss, Roland and Zhu, Liming},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.7.166},
  URN =		{urn:nbn:de:0030-drops-197793},
  doi =		{10.4230/DagRep.13.7.166},
  annote =	{Keywords: Architecting ML-enabled Systems, ML for Software Architecture, Software Architecture for ML, Machine Learning, Software Architecture, Software Engineering}
}

Document

Complete Volume

DOI: 10.4230/LIPIcs.CP.2023

LIPIcs, Volume 280, CP 2023, Complete Volume

Authors: Roland H. C. Yap

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

LIPIcs, Volume 280, CP 2023, Complete Volume

Cite as

29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 1-808, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Proceedings{yap:LIPIcs.CP.2023,
  title =	{{LIPIcs, Volume 280, CP 2023, Complete Volume}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{1--808},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023},
  URN =		{urn:nbn:de:0030-drops-190365},
  doi =		{10.4230/LIPIcs.CP.2023},
  annote =	{Keywords: LIPIcs, Volume 280, CP 2023, Complete Volume}
}

Document

Front Matter

DOI: 10.4230/LIPIcs.CP.2023.0

Front Matter, Table of Contents, Preface, Conference Organization

Authors: Roland H. C. Yap

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

Front Matter, Table of Contents, Preface, Conference Organization

Cite as

29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 0:i-0:xx, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{yap:LIPIcs.CP.2023.0,
  author =	{Yap, Roland H. C.},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{0:i--0:xx},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.0},
  URN =		{urn:nbn:de:0030-drops-190372},
  doi =		{10.4230/LIPIcs.CP.2023.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}

Document

Invited Talk

DOI: 10.4230/LIPIcs.CP.2023.1

Beyond Optimal Solutions for Real-World Problems (Invited Talk)

Authors: Maria Garcia de la Banda

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

Combinatorial optimisation technology has come a long way. We now have mature high-level modelling languages in which to specify a model of the particular problem of interest [Nethercote et al., 2007; Frisch et al., 2008; Van Hentenryck, 1999; Fourer et al., 1990]; robust complete solvers in each major constraint paradigm, including Constraint Programming (CP), MaxSAT [Jessica Davies and Fahiem Bacchus, 2011; Alexey Ignatiev et al., 2019], and Mixed Integer Programming (MIP); effective incomplete search techniques that can easily be combined with complete solvers to speed up the search such as Large Neighbourhood Search [Paul Shaw, 1998]; and enough general knowledge about modelling techniques to understand the need for our models to incorporate components such as global constraints [Willem-Jan van Hoeve and Irit Katriel, 2006], symmetry constraints [Ian P. Gent et al., 2006], and more. All this has significantly reduced the amount of knowledge required to apply this technology successfully to the many different combinatorial optimisation problems that permeate our society. And yet, not many organisations use such advanced optimisation technology; instead, they often rely on the solutions provided by problem-specific algorithms that are implemented in traditional imperative languages and lack any of the above advances. Further, while advanced optimisation technology is particularly suitable for the kind of complex human-in-the-loop decision-making problems that occur in critical sectors of our society, including health, transport, energy, disaster management, environment and finance, these decisions are often still made by people with little or no technological support. In this extended abstract I argue that to change this state of affairs, our research focus needs to change from improving the technology on its own, to improving it so that users can better trust, use, and maintain the optimisation systems that we develop with it. The rest of this extended abstract discusses my personal experiences and opinion on these three points. Trust I highlight trust (which focuses on the user’s point of view) rather than trustworthiness (which is a characteristic of the software itself) because I think it is the former rather than the latter that is at stake for the adoption of optimisation technology. One of the biggest hurdles I have found for trust in the context of optimisation systems is for the domain experts to (feel like they) understand the underlying model. While many users will never do (or have to), I believe it is key for domain experts to have a high-level understanding of the constraints in the model, since their (dis)trust will likely spread through the organisation, impacting the adoption of the system. Thanks to the use of high-level modelling languages in CP, our group has achieved this [Matthias Klapperstueck et al., 2023] by documenting the constraints in a language the user knows (mathematics) and linking each constraint to the particular part of the model that implements it (via comments). While domain experts do not completely understand the model, the similarity between the format they understand (mathematics) and the model constraint has helped them verify our perception of their problem and improved their trust in the model. However, more needs to be done in this direction via the development of formal techniques. For example, our group is exploring the use of domain-specific languages [Hudak, 1997] as a bridge between domain experts and modellers that helps both trust and maintenance (see later). This [Sameela Suharshani Wijesundara et al., 2023] and other approaches need to be explored. A very significant source of trust for our domain experts (and of trustworthiness for the software) has been the development of two different models implemented by two different people for the same problem [Matthias Klapperstueck et al., 2023]. While this can be seen as a prohibitively expensive exercise, it did not take that long once the first model was mature, is a good way to onboard new optimisation team members, and has helped up detect not only bugs but also differences in the interpretation of domain expert information. For optimisation problems where it is not possible to verify the optimality (or even correctness) of the solution, we see such redundant modelling as the only solution for now. Interestingly, a significant step forward in obtaining the trust of our domain experts has been the generation of an optimality gap whenever an optimal solution could not be found due to time constraints. While explaining this concept took time, once understood it has boosted their trust, particularly when tackling problems where the solution is not easy verifiable or when approximated models/data are used (needed for speed, see later). This makes it difficult to work with CP and SAT solvers, as they usually lack tight lower bounds. Finally, trust is often developed through the use of the system, which I discuss below. Use Usability is known to be key for the deployment of software systems. By "system" in our context, I refer to the combination of the problem model(s), the associated solver(s) and, importantly, the User Interface (UI) that often integrates them and is fundamental to their success. In addition to the traditional usability characteristics of software systems, I believe an optimisation system requires particular care in the following areas. Interaction, i.e., the system must allow users to interact with the UI not only to provide and modify the input data, but also to modify the constraints (at the very least by turning some on/off) as well as explore and compare solutions, as argued in [David Meignan et al., 2015; Jie Liu et al., 2021]. Incremental compilers and solvers would significantly help in making this easier, as well as generic ways for the UIs to communicate with them. Conflict resolution, that is, ensuring the system can not only detect infeasible instances, but also support users in understanding the data/constraints that cause infeasibility and how to modify the instance to make it feasible. Any interactive optimisation system that has users, will likely have conflicts. Thus, it is mandatory for CP to improve its conflict resolution technology which, while existent [João Marques-Silva and Alessandro Previti, 2014; Lauffer and Topcu, 2019; Ilankaikone Senthooran et al., 2023], is not widespread and it is often still problem-dependent, overwhelming (in the number of constraints shown to the user) and slow. Without it, users will be "stumped" when (rather than if) infeasibility is reached. Solution diversity, that is, supporting users in obtaining a diverse set of (close-to-optimal) solutions, where diversity is measured by a user-provided metric modelled somehow. While some solver-independent technology has been developed and implemented for this [Emmanuel Hebrard et al., 2005; Thierry Petit and Andrew C. Trapp, 2015; Linnea Ingmar et al., 2020], it should be easier to use and more widespread. Further, it requires sophisticated solution comparison capabilities and, importantly, for optimal solutions to be found in seconds rather than hours. This brings me to speed, an area where CP solvers are falling behind. Most of our research group applications now use MIP solvers due to the need for floats (which precludes us from using learning solvers such as Chuffed [Geoffrey Chu, 2013]), but also to the lack of effective warm-start processes that are available in MIP solvers. Interestingly, data and model approximations have been proved to achieve orders of magnitude speedups with small reductions in optimality [Matthias Klapperstueck et al., 2023]. Developing generic (i.e., problem independent) accurate approximations would be extremely useful for complex decision systems. Other areas where I think generic CP methods are worth investigating more include dealing with uncertainty and online problems, ensuring solution fairness (even if it is over time), and studying predict + optimise approaches. Maintain I know very few papers devoted to the issue of maintenance in optimisation technology. While this may be due to my lack of knowledge, I suspect it is also due to the limited adoption of optimisation technology. While the issues in this area are again common to other software systems, I believe the solutions for CP require special attention. For example, the issue of changes in user requirements (that our research group calls problem drift) seems particularly prevalent in decision-making systems, as such problems can evolve rapidly due to unforeseen circumstances. This can make optimisation systems obsolete faster than expected. Our research group has proposed to tackle problem drift by developing a requirements model implemented in the above-mentioned MDSLs and created by both domain experts and modellers that, when modified re-generates parts of the model to support the modifications [Sameela Suharshani Wijesundara et al., 2023]. This and other approaches such as the creation of reusable models components [Sophia Saller and Jana Koehler, 2022; Toby Walsh, 2003], or instantiatable classes for common problem domains, are worth investigating.

Cite as

Maria Garcia de la Banda. Beyond Optimal Solutions for Real-World Problems (Invited Talk). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 1:1-1:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{garciadelabanda:LIPIcs.CP.2023.1,
  author =	{Garcia de la Banda, Maria},
  title =	{{Beyond Optimal Solutions for Real-World Problems}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{1:1--1:4},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.1},
  URN =		{urn:nbn:de:0030-drops-190384},
  doi =		{10.4230/LIPIcs.CP.2023.1},
  annote =	{Keywords: Combinatorial optimisation systems, usability, trust, maintenance}
}

Document

Invited Talk

DOI: 10.4230/LIPIcs.CP.2023.2

A Tale of Two Cities: Teaching CP with Story-Telling (Invited Talk)

Authors: Jimmy H.M. Lee

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

This presentation is all about story-telling. It tells the story, the pedagogical innovations and experience of the co-development of three MOOCs on the subject of "Modeling and Solving Discrete Optimization Problems” by The Chinese University of Hong Kong (CUHK) and the University of Melbourne, each with unique culture and tradition. The MOOCs feature the Fable-based Learning approach, which is a form of problem-based learning encapsulated in a story plot. Each MOOC video begins with an animation that follows a story adapted from a Chinese classic. The heroes of the story encounter various optimization problems requiring technical assistance from two professors from modern time via a magical tablet granted to the heroes by a genie old man. The animation thus sets the stage for lecturing modeling and solving techniques. The new pedagogy provides a movie-like immersive experience to the learners, and aims at increasing learners’ motivation and interests as well as situating them in a coherent learning context. In addition to scriptwriting, animation production and embedding the teaching materials in the story plot, another challenge of the project is the remote distance between the two institutions as well as the need to produce all teaching materials in both (Mandarin) Chinese and English to cater for different geographic learning needs. The project and production spanned across 2016 and 2017. The MOOCs have been running recurrently on Coursera since January, 2017. We present learner statistics and feedback, and discuss our experience and preliminary observations of adopting the online materials in a Flipped Classroom setting at CUHK.

Cite as

Jimmy H.M. Lee. A Tale of Two Cities: Teaching CP with Story-Telling (Invited Talk). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, p. 2:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{lee:LIPIcs.CP.2023.2,
  author =	{Lee, Jimmy H.M.},
  title =	{{A Tale of Two Cities: Teaching CP with Story-Telling}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{2:1--2:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.2},
  URN =		{urn:nbn:de:0030-drops-190395},
  doi =		{10.4230/LIPIcs.CP.2023.2},
  annote =	{Keywords: Constraint Programming, MOOCs, Fable-based Learning}
}

Document

Invited Talk

DOI: 10.4230/LIPIcs.CP.2023.3

The CP-SAT-LP Solver (Invited Talk)

Authors: Laurent Perron, Frédéric Didier, and Steven Gay

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

The CP-SAT-LP solver is developed by the Operations Research team at Google and is part of the OR-Tools [Laurent Perron and Vincent Furnon, 2023] open-source optimization suite. It is an implementation of a purely integral Constraint Programming solver on top of a SAT solver using Lazy Clause Generation [Stuckey, 2010]. It draws its inspiration from the chuffed solver [Geoffrey Chu et al., 2023], and from the CP 2013 plenary by Peter Stuckey on Lazy Clause Generation [Stuckey, 2013]. The CP-SAT-LP solver improves upon the chuffed solver [Geoffrey Chu et al., 2023] in two main directions. First, it uses a simplex alongside the SAT engine. Second, it implements and relies upon a portfolio of diverse workers for its search part. The use of the simplex brings the obvious advantages of a linear relaxation on the linear part of the full model. It also started the integration of MIP technology into CP-SAT-LP. This is a huge endeavour, as MIP solvers are mature and complex. It includes presolve - which was already a part of CP-SAT -, dual reductions, specific branching rules, cuts, reduced cost fixing, and more advanced techniques. It also allows to integrate tightly the research from the Scheduling on MIP community [Balas, 1985; Applegate and Cook, 1991; Maurice Queyranne, 1993] along with the most advanced scheduling algorithms [Vilím, 2011]. This has enabled breakthroughs in solving and proving hard scheduling instances of the Job-Shop problems [Ding et al., 2019] and Resource Constraint Project Scheduling Problems [Rainer Kolisch and Arno Sprecher, 1997; Artigues et al., 2008]. Using a portfolio of different workers makes it easier to try new ideas and to incorporate orthogonal techniques with little complication, except controlling the explosion of potential workers. These workers can be categorized along multiple criteria like finding primal solutions - either using complete solvers, Local Search [Luteberget and Sartor, 2023] or Large Neighborhood Search [Paul Shaw, 1998] -, improving dual bounds, trying to reduce the problem with the help of continuous probing. This diversity of behaviors has increased the robustness of the solver, while the continuous sharing of information between workers has produced massive speedups when running multiple workers in parallel. All in all, CP-SAT-LP is a state-of-the-art solver, with unsurpassed performance in the Constraint Programming community, breakthrough results on Scheduling benchmarks (with the closure of many open problems), and competitive results with the best MIP solvers (on purely integral problems).

Cite as

Laurent Perron, Frédéric Didier, and Steven Gay. The CP-SAT-LP Solver (Invited Talk). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 3:1-3:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{perron_et_al:LIPIcs.CP.2023.3,
  author =	{Perron, Laurent and Didier, Fr\'{e}d\'{e}ric and Gay, Steven},
  title =	{{The CP-SAT-LP Solver}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{3:1--3:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.3},
  URN =		{urn:nbn:de:0030-drops-190405},
  doi =		{10.4230/LIPIcs.CP.2023.3},
  annote =	{Keywords: Constraint Programming, Operations Research, Sat Solver}
}

Document

Invited Talk

DOI: 10.4230/LIPIcs.CP.2023.4

Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration (Invited Talk)

Authors: Thomas Schiex

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

The use of discrete optimization, including Constraint Programming, for designing objects that we completely understand is quite usual. In this talk, I'll show how designing specific biomolecules (proteins) raises new challenges, requiring solving problems that combine precise design targets, approximate laws, and design rules that can be deep-learned from data.

Cite as

Thomas Schiex. Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration (Invited Talk). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 4:1-4:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{schiex:LIPIcs.CP.2023.4,
  author =	{Schiex, Thomas},
  title =	{{Coupling CP with Deep Learning for Molecular Design and SARS-CoV2 Variants Exploration}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{4:1--4:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.4},
  URN =		{urn:nbn:de:0030-drops-190415},
  doi =		{10.4230/LIPIcs.CP.2023.4},
  annote =	{Keywords: graphical models, deep learning, constraint programming, cost function networks, random Markov fields, decision-focused learning, protein design}
}

Document

Invited Talk

DOI: 10.4230/LIPIcs.CP.2023.5

CP Solver Design for Maximum CPU Utilization (Invited Talk)

Authors: Petr Vilím

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

In this talk, I explain how to improve the performance of a solver without focusing on algorithms, search, propagation or parallelism. Performance is achieved instead with better CPU utilization, efficient code and more precise design of the solver itself. In the words of Fedor G. Pikus [Pikus, 2021], the time of "performance taking care of itself" is over. In today’s hardware the number of cores is increasing while the CPU clock speed has reached a plateau. Main memory access is slow in comparison to the CPU. And despite multiple memory cache levels, the CPU can easily become idle waiting for data from the memory, slowing down the computation considerably. Unfortunately, those trends are probably not going to change in the near future. For those reasons we are witnessing revived interest in efficient code and performance-centered software design, especially in areas where the performance is critical: computer games, compilers, internet browsers, language interpreters (e.g. JavaScript or Python), etc. The good news is that many of the tricks used in the above-mentioned areas, can be used in constraint programming as well. The bad news is that the performance has to be taken into account from the very beginning of the design. It is not possible to add it easily later. Sometimes, better performance can be achieved only by radical shifts in the design such as from object-oriented to data-oriented programming. The design of a CP solver is not an exception in this regard. Without the efficient core of the CP solver, it is not possible to write truly efficient propagation or search algorithms. On the other hand, all algorithms in the solver must take the design of the solver into account and leverage it. In this talk, I will describe what I consider the most important aspects of the design of ScheduleOpt Optal solver. I will concentrate on the performance, but I will also mention other aspects such as ease of use, maintainability, and testing.

Cite as

Petr Vilím. CP Solver Design for Maximum CPU Utilization (Invited Talk). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, p. 5:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{vilim:LIPIcs.CP.2023.5,
  author =	{Vil{\'\i}m, Petr},
  title =	{{CP Solver Design for Maximum CPU Utilization}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{5:1--5:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.5},
  URN =		{urn:nbn:de:0030-drops-190425},
  doi =		{10.4230/LIPIcs.CP.2023.5},
  annote =	{Keywords: Constraint Programming, Software Design, Efficient Code}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.6

Optimization of Short-Term Underground Mine Planning Using Constraint Programming

Authors: Younes Aalian, Gilles Pesant, and Michel Gamache

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

Short-term underground mine planning problems are often difficult to solve due to the large number of activities and diverse machine types to be scheduled, as well as multiple operational constraints. This paper presents a Constraint Programming (CP) model to optimize short-term scheduling for the Meliadine underground gold mine in Nunavut, Canada, taking into consideration operational constraints and the daily development and production targets of the mine plan. To evaluate the efficacy of the developed CP short-term planning model, we compare schedules generated by the CP model with the ones created manually by the mine planner for two real data sets. Results demonstrate that the CP model outperforms the manual approach by generating more efficient schedules with lower makespans.

Cite as

Younes Aalian, Gilles Pesant, and Michel Gamache. Optimization of Short-Term Underground Mine Planning Using Constraint Programming. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{aalian_et_al:LIPIcs.CP.2023.6,
  author =	{Aalian, Younes and Pesant, Gilles and Gamache, Michel},
  title =	{{Optimization of Short-Term Underground Mine Planning Using Constraint Programming}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{6:1--6:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.6},
  URN =		{urn:nbn:de:0030-drops-190430},
  doi =		{10.4230/LIPIcs.CP.2023.6},
  annote =	{Keywords: Mine planning, Constraint Programming, Short-term planning, Underground mine, Scheduling}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.7

Exploiting Configurations of MaxSAT Solvers

Authors: Josep Alòs, Carlos Ansótegui, Josep M. Salvia, and Eduard Torres

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

In this paper, we describe how we can effectively exploit alternative parameter configurations to a MaxSAT solver. We describe how these configurations can be computed in the context of MaxSAT. In particular, we experimentally show how to easily combine configurations of a non-competitive solver to obtain a better solving approach.

Cite as

Josep Alòs, Carlos Ansótegui, Josep M. Salvia, and Eduard Torres. Exploiting Configurations of MaxSAT Solvers. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 7:1-7:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{alos_et_al:LIPIcs.CP.2023.7,
  author =	{Al\`{o}s, Josep and Ans\'{o}tegui, Carlos and Salvia, Josep M. and Torres, Eduard},
  title =	{{Exploiting Configurations of MaxSAT Solvers}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{7:1--7:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.7},
  URN =		{urn:nbn:de:0030-drops-190443},
  doi =		{10.4230/LIPIcs.CP.2023.7},
  annote =	{Keywords: maximum satisfiability, maxsat evaluation, automatic configuration}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.8

Symmetries for Cube-And-Conquer in Finite Model Finding

Authors: João Araújo, Choiwah Chow, and Mikoláš Janota

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

The cube-and-conquer paradigm enables massive parallelization of SAT solvers, which has proven to be crucial in solving highly combinatorial problems. In this paper, we apply the paradigm in the context of finite model finding, where we show that isomorphic cubes can be discarded since they lead to isomorphic models. However, we are faced with the complication that a well-known technique, the Least Number Heuristic (LNH), already exists in finite model finders to effectively prune (some) isomorphic models from the search. Therefore, it needs to be shown that isomorphic cubes still can be discarded when the LNH is used. The presented ideas are incorporated into the finite model finder Mace4, where we demonstrate significant improvements in model enumeration.

Cite as

João Araújo, Choiwah Chow, and Mikoláš Janota. Symmetries for Cube-And-Conquer in Finite Model Finding. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 8:1-8:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{araujo_et_al:LIPIcs.CP.2023.8,
  author =	{Ara\'{u}jo, Jo\~{a}o and Chow, Choiwah and Janota, Mikol\'{a}\v{s}},
  title =	{{Symmetries for Cube-And-Conquer in Finite Model Finding}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{8:1--8:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.8},
  URN =		{urn:nbn:de:0030-drops-190455},
  doi =		{10.4230/LIPIcs.CP.2023.8},
  annote =	{Keywords: finite model enumeration, cube-and-conquer, symmetry-breaking, parallel algorithm, least number heuristic}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.9

Guiding Backtrack Search by Tracking Variables During Constraint Propagation

Authors: Gilles Audemard, Christophe Lecoutre, and Charles Prud'homme

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

It is well-known that variable ordering heuristics play a central role in solving efficiently Constraint Satisfaction Problem (CSP) instances. From the early 80’s, and during more than two decades, the dynamic variable ordering heuristic selecting the variable with the smallest domain was clearly prevailing. Then, from the mid 2000’s, some adaptive heuristics have been introduced: their principle is to collect some useful information during the search process in order to take better informed decisions. Among those adaptive heuristics, wdeg/dom (and its variants) remains particularly robust. In this paper, we introduce an original heuristic based on the midway processing of failing executions of constraint propagation: this heuristic called pick/dom tracks the variables that are directly involved in the process of constraint propagation, when ending with a conflict. The robustness of this new heuristic is demonstrated from a large experimentation conducted with the constraint solver ACE. Interestingly enough, one can observe some complementary between the early, midway and late forms of processing of conflicts.

Cite as

Gilles Audemard, Christophe Lecoutre, and Charles Prud'homme. Guiding Backtrack Search by Tracking Variables During Constraint Propagation. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 9:1-9:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{audemard_et_al:LIPIcs.CP.2023.9,
  author =	{Audemard, Gilles and Lecoutre, Christophe and Prud'homme, Charles},
  title =	{{Guiding Backtrack Search by Tracking Variables During Constraint Propagation}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{9:1--9:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.9},
  URN =		{urn:nbn:de:0030-drops-190461},
  doi =		{10.4230/LIPIcs.CP.2023.9},
  annote =	{Keywords: Variable Ordering Heuristics, Variable Weighting}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.10

Incremental Constrained Clustering by Minimal Weighted Modification

Authors: Aymeric Beauchamp, Thi-Bich-Hanh Dao, Samir Loudni, and Christel Vrain

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

Clustering is a well-known task in Data Mining that aims at grouping data instances according to their similarity. It is an exploratory and unsupervised task whose results depend on many parameters, often requiring the expert to iterate several times before satisfaction. Constrained clustering has been introduced for better modeling the expectations of the expert. Nevertheless constrained clustering is not yet sufficient since it usually requires the constraints to be given before the clustering process. In this paper we address a more general problem that aims at modeling the exploratory clustering process, through a sequence of clustering modifications where expert constraints are added on the fly. We present an incremental constrained clustering framework integrating active query strategies and a Constraint Programming model to fit the expert expectations while preserving the stability of the partition, so that the expert can understand the process and apprehend its impact. Our model supports instance and group-level constraints, which can be relaxed. Experiments on reference datasets and a case study related to the analysis of satellite image time series show the relevance of our framework.

Cite as

Aymeric Beauchamp, Thi-Bich-Hanh Dao, Samir Loudni, and Christel Vrain. Incremental Constrained Clustering by Minimal Weighted Modification. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 10:1-10:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{beauchamp_et_al:LIPIcs.CP.2023.10,
  author =	{Beauchamp, Aymeric and Dao, Thi-Bich-Hanh and Loudni, Samir and Vrain, Christel},
  title =	{{Incremental Constrained Clustering by Minimal Weighted Modification}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{10:1--10:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.10},
  URN =		{urn:nbn:de:0030-drops-190478},
  doi =		{10.4230/LIPIcs.CP.2023.10},
  annote =	{Keywords: Incremental constrained clustering, Constrained optimization problem, User feedback}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.11

Simplifying Step-Wise Explanation Sequences

Authors: Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, and Tias Guns

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

Explaining constraint programs is useful for debugging an unsatisfiable program, to understand why a given solution is optimal, or to understand how to find a unique solution. A recently proposed framework for explaining constraint programs works well to explain the unique solution to a problem step by step. It can also be used to step-wise explain why a model is unsatisfiable, but this may create redundant steps and introduce superfluous information into the explanation sequence. This paper proposes methods to simplify a (step-wise) explanation sequence, to generate simple steps that together form a short, interpretable sequence. We propose an algorithm to greedily construct an initial sequence and two filtering algorithms that eliminate redundant steps and unnecessarily complex parts of explanation sequences. Experiments on diverse benchmark instances show that our techniques can significantly simplify step-wise explanation sequences.

Cite as

Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, and Tias Guns. Simplifying Step-Wise Explanation Sequences. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{bleukx_et_al:LIPIcs.CP.2023.11,
  author =	{Bleukx, Ignace and Devriendt, Jo and Gamba, Emilio and Bogaerts, Bart and Guns, Tias},
  title =	{{Simplifying Step-Wise Explanation Sequences}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{11:1--11:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.11},
  URN =		{urn:nbn:de:0030-drops-190489},
  doi =		{10.4230/LIPIcs.CP.2023.11},
  annote =	{Keywords: explanation, deduction, constraint programming, propagation}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.12

Towards More Efficient Local Search for Pseudo-Boolean Optimization

Authors: Yi Chu, Shaowei Cai, Chuan Luo, Zhendong Lei, and Cong Peng

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract

Pseudo-Boolean (PB) constraints are highly expressive, and many combinatorial optimization problems can be modeled using pseudo-Boolean optimization (PBO). It is recognized that stochastic local search (SLS) is a powerful paradigm for solving combinatorial optimization problems, but the development of SLS for solving PBO is still in its infancy. In this paper, we develop an effective SLS algorithm for solving PBO, dubbed NuPBO, which introduces a novel scoring function for PB constraints and a new weighting scheme. We conduct experiments on a broad range of six public benchmarks, including three real-world benchmarks, a benchmark from PB competition, an integer linear programming optimization benchmark, and a crafted combinatorial benchmark, to compare NuPBO against five state-of-the-art competitors, including a recently-proposed SLS PBO solver LS-PBO, two complete PB solvers PBO-IHS and RoundingSat, and two mixed integer programming (MIP) solvers Gurobi and SCIP. NuPBO has been exhibited to perform best on these three real-world benchmarks. On the other three benchmarks, NuPBO shows competitive performance compared to state-of-the-art competitors, and it significantly outperforms LS-PBO, indicating that NuPBO greatly advances the state of the art in SLS for solving PBO.

Cite as

Yi Chu, Shaowei Cai, Chuan Luo, Zhendong Lei, and Cong Peng. Towards More Efficient Local Search for Pseudo-Boolean Optimization. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 12:1-12:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{chu_et_al:LIPIcs.CP.2023.12,
  author =	{Chu, Yi and Cai, Shaowei and Luo, Chuan and Lei, Zhendong and Peng, Cong},
  title =	{{Towards More Efficient Local Search for Pseudo-Boolean Optimization}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{12:1--12:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.12},
  URN =		{urn:nbn:de:0030-drops-190490},
  doi =		{10.4230/LIPIcs.CP.2023.12},
  annote =	{Keywords: Pseudo-Boolean Optimization, Stochastic Local Search, Scoring Function, Weighting Scheme}
}

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