The Workshop

The Nordic Network for researchers and practitioners of Constraint programming (NordConsNet) kindly invites you to participate in the yearly NordConsNet Workshop. The purpose of the workshop is to learn about ongoing research in Constraint Programming and optimisation, existing projects and products, and further development of the network. NordConsNet Workshop 2019 is the 18th edition of this annual event.


The workshop will be held on Tuesday, 21.05.2019. The workshop program will start at 10:00 (see program below), but we invite everyone to arrive earlier for a nice cup of coffee and a chat with the other participants.


The workshop will take place at Simula Research Laboratory, Martin Linges vei 25, 1364 Fornebu, Norway. How to get to Simula. Simula is located in the Technopolis building. Simula’s main entrance is on the 3rd floor, turning right up the stairs from the Technopolis main reception area.


The presentation abstracts are below on this page.

Time Presenter Title Slides
09:30 - 10:00   Morning Coffee  
10:00 - 10:10 Helge Spieker Welcome  
10:10 - 10:30 Arnaud Gotlieb Simula Research Laboratory: Constraint Programming for Software Engineering  
10:30 - 11:00 Gustav Björdal Generating Compound Moves in Local Search by Hybridisation with Systematic Search PDF
11:00 - 11:30 Jan Elffers On-the-fly cardinality detection  
11:30 - 12:00 Stephan Gocht On Division Versus Saturation in Pseudo-Boolean Solving PDF
12:00 - 13:30   Lunch  
13:30 - 14:00 Matti Järvisalo CP at the University of Helsinki  
14:00 - 14:30 Nadjib Lazaar Constraint Acquisition PDF
14:30 - 15:00 Sabino Roselli On SMT Solvers and Job Shop Problems PDF
15:00 - 15:30   Coffee Break  
15:30 - 16:00 Mathieu Collet Stress Testing of Single-Arm Robots Through Constraint-Based Generation of Continuous Trajectories  
16:00 - 16:30 Jo Devriendt Leveraging LP solving for PB solving PDF
16:30 - 17:00 Jakob Nordström Subgraph Isomorphism Meets Cutting Planes PDF
17:00 - 17:10   Closing  
18:00   Dinner at Scandic Fornebu  


Simula Research Laboratory: Constraint Programming for Software Engineering.

Arnaud Gotlieb (Simula Research Laboratory)

Since 2001, Simula emphasizes and promote basic research for tackling scientific challenges with long-term impact and of genuine importance to real life. Among the carefully selected research fields investigated from its origin, Software Engineering has been a wide and fruitful area to explore with techniques coming from Artificial Intelligence, namely Constraint Programming (CP) and Machine Learning (ML). This talk will briefly review the various research activities held in the side of CP and will present some of the most significant Simula’s innovative projects.

Gustav Björdal (Uppsala University)

A black-box local-search backend to MiniZinc automatically infers from the structure of a declarative model a neighbourhood, heuristic, and meta-heuristic. These ingredients are then provided to a local-search solver. However, such a backend can perform poorly due to model structure that is inappropriate for local search, for example when it considers moves modifying only variables that represent auxiliary information.

Towards overcoming such inefficiency, we propose compound-move generation, an extension to local-search solvers that uses a complete-search solver in order to augment moves modifying non-auxiliary variables so that they also modify auxiliary ones.

In this presentation I will give a motivating example for this work and an overview of our approach.

On-the-fly cardinality detection

Jan Elffers (KTH)

We improve performance of pseudo-Boolean SAT solvers by recovering cardinality constraints during proof search. During unit propagation, we derive additional short clauses, which are then used as building blocks for constructing cardinality constraints during conflict analysis. Our experiments comparing this approach to the previous work [Biere et al., SAT 2014] indicate that our new algorithm performs significantly better.

On Division Versus Saturation in Pseudo-Boolean Solving

Stephan Gocht (KTH)

The conflict-driven clause learning (CDCL) paradigm has revolutionized SAT solving over the last two decades. Extending this approach to pseudo-Boolean (PB) solvers doing 0-1 linear programming holds the promise of further exponential improvements in theory, but intriguingly such gains have not materialized in practice.

In this talk, I will give a brief introduction to the cutting planes method used for 0-1 linear programming. I will then discuss how different restrictions of the method in practice affects the theoretical reasoning power, focusing on recent results that are joint work with Jakob Nordström and Amir Yehudayoff.

Constraint Reasoning and Optimization at University of Helsinki

Matti Järvisalo (University of Helsinki)

The Constraint Reasoning and Optimization Group at the University of Helsinki, Finland, focuses on the development of Boolean satisfiability (SAT) solving techniques and SAT-based and other discrete search and optimization procedures for exactly solving intrinsically hard (NP-complete and beyond-NP) real-world problems. In this talk I will give an overview of some of the key contributions and main research directions of the group, with more details (as time permits) on hybrid SAT-IP approaches and applications of constraint solvers in the context of structure learning for probabilistic graphical models.

Constraint Acquisition

Nadjib Lazaar (LIRMM – CNRS, U. of Montpellier, France)

Constraint programming is used to model and solve complex combinatorial problems.

The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology.

Several approaches have been proposed to assist the non-expert user in the modeling task. In this talk, I will present the recent results on constraint acquisition obtained by the Coconut team and their collaborators. In a first part I will show how to learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm, called QUACQ, that, given a negative example, finds a constraint of the target network in a number of queries logarithmic in the size of the example. In a second part, I will show how to make constraint acquisition more efficient in practice (new kind of queries, the use of some background knowledge, more elicitation…).

On SMT Solvers and Job Shop Problems

Sabino Roselli (Chalmers)

The optimal assignment of jobs to machines is a common problem when implementing automated production systems. A specific variant of this category is the job-shop scheduling problem (JSP) that is known to belong to the class of NP-hard problems. JSPs are typically either formulated as Mixed Integer Linear Programming (MILP) problems and solved by general-purpose-MILP solvers or approached using heuristic algorithms specifically designed for the purpose. During the last decade a new approach, satisfiability (SAT), led to develop solvers with incredible abilities in finding feasible solutions for hard combinatorial problems on Boolean variables. Moreover, an extension of SAT, Satisfability Modulo Theory (SMT), allows to formulate constraints involving linear operations over integers and reals and some SMT-solvers have been also extended with an optimizing tool. Since the JSP is a well-known hard combinatorial problem, it is interesting to evaluate how SMT-solvers perform in solving it and how they compare to traditional MILP-solvers. We therefore evaluate state-of-the-art MILP and SMT solvers on benchmark JSP instances and find that general-purpose opensource SMT-solvers are competitive against commercial MILP-solvers.

Stress Testing of Single-Arm Robots Through Constraint-Based Generation of Continuous Trajectories

Mathieu Collet (Simula Research Laboratory)


Leveraging LP solving for PB solving

Jo Devriendt (KTH)

Pseudo-Boolean (PB) solvers are algorithms for 0-1 integer linear programming, with state-of-the-art PB algorithms mimicking the succesful conflict-driven clause-learning (CDCL) paradigm from Boolean satisfiability (SAT). However, although PB solver can be exponentially stronger than CDCL solver, they can also get hopelessly stuck in rationally infeasible parts of the search space, where it should be easy to see that there are no solutions.

To remedy this, we integrate a linear programming (LP) solver in the PB solver algorithm. A straightforward hybrid algorithm encounters two problems. First, running an LP solver might starve the PB solver, as calling an LP solver is orders of magnitudes costlier than executing an iteration in the PB solver search loop. Second, most LP solvers use imprecise floating-point arithmetic, which can cause problems with soundness. We propose a hybrid PB+LP approach that addresses these issues. We present experimental results of an implementation that show large speed-ups on certain benchmark families and only incur moderate overhead in general.

Subgraph Isomorphism Meets Cutting Planes

Jakob Nordström (KTH)

Although subgraph isomorphism — deciding whether a smaller pattern graph occurs as a subgraph in a larger target graph — is an NP-complete problem, it can often be solved quite efficiently in practice using highly optimized, and nontrivial, constraint programming techniques. We recently stumbled over the finding that the reasoning used in state-of-the-art solvers, such as the Glasgow Subgraph Solver, is captured by the cutting planes proof system studied in computational complexity theory. This opens up (at least) two quite intriguing directions to explore:

(1) Graph solvers could be enhanced to do “proof logging” of their reasoning, hopefully with not too large overhead, to produce efficiently verifiable proofs that the end result is correct (verifiable by a simple stand-alone program that knows nothing about graphs).

(2) The learning mechanism in so-called conflict-driven pseudo-Boolean solvers could be harnessed to add learned constraints (a.k.a. no-goods) that could potentially speed up the algorithm exponentially.

Another natural question going forward is whether this could be more broadly applicable to constraint programming algorithms for other hard combinatorial problems.

This is joint work in progress by (a dynamic super-/subset of) Jan Elffers, Stephan Gocht, Ciaran McCreesh, and Jakob Nordström.


The NordConsNet Workshop 2019 is chaired by Arnaud Gotlieb and Helge Spieker (both Certus SFI & Simula Research Laboratory).

Registration (Closed)

To register please send an email to Helge Spieker. Registration including lunch and catering must be send prior to May, 3. If you register after that date, you are still very welcome to attend any talks during the day, just be mindful not to enter during a talk and don’t expect catering. There are no registration fees for the workshop.

Please forward this information to anyone who might be interested in this workshop but is not yet on the NordConsNet mailing list: they can subscribe to it by applying to Justin Pearson.

Submit a Presentation Proposal (Closed)

We hope for your participation, and highly encourage you to submit a proposal for a presentation of your ongoing work, recent results, or of a relevant discussion topic. There are no paper submissions, reviews, or proceedings, hence recent conference/journal papers may also be presented. Please contact Helge Spieker if you would like to present.