当前期刊: Constraints Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
  • Efficient multiple constraint acquisition
    Constraints (IF 1.167) Pub Date : 2020-09-17
    Dimosthenis C. Tsouros, Kostas Stergiou

    Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms

  • The flexible and real-time commute trip sharing problems
    Constraints (IF 1.167) Pub Date : 2020-08-19
    Mohd. Hafiz Hasan, Pascal Van Hentenryck

    The Commute Trip Sharing Problem (CTSP) was introduced to remove parking pressure on cities, as well as corporate and university campuses. Its goal is to reduce the number of vehicles being used for daily commuting activities. Given a set of inbound and outbound requests, which consists of origin and destination pairs and their departure and return times, the CTSP assigns riders and a driver, as well

  • Non-local configuration of component interfaces by constraint satisfaction
    Constraints (IF 1.167) Pub Date : 2020-08-05
    Olga Tveretina, Pavel Zaichenkov, Alex Shafarenko

    Service-oriented computing is the paradigm that utilises services as fundamental elements for developing applications. Service composition, where data consistency becomes especially important, is still a key challenge for service-oriented computing. We maintain that there is one aspect of Web service communication on the data conformance side that has so far escaped the researchers attention. Aggregation

  • Invariants for time-series constraints
    Constraints (IF 1.167) Pub Date : 2020-07-18
    Ekaterina Arafailova, Nicolas Beldiceanu, Helmut Simonis

    Many constraints restricting the result of some computations over an integer sequence can be compactly represented by counter automata. We improve the propagation of the conjunction of such constraints on the same sequence by synthesising a database of linear and non-linear invariants using their counter-automaton representation. The obtained invariants are formulae parameterised by the sequence length

  • XCSP 3 and its ecosystem
    Constraints (IF 1.167) Pub Date : 2020-02-06
    Gilles Audemard; Frédéric Boussemart; Christophe Lecoutre; Cédric Piette; Olivier Roussel

    In this paper, we present a summary of XCSP3, together with its ecosystem. XCSP3 is a format used to build integrated representations of combinatorial constrained problems. Interestingly, XCSP3 preserves the structure of models, by handling arrays of variables and groups/blocks of constraints, which makes it rather unique in the literature. Furthermore, the ecosystem of XCSP3 is well supplied: it includes

  • Integrated integer programming and decision diagram search tree with an application to the maximum independent set problem
    Constraints (IF 1.167) Pub Date : 2020-01-15
    Jaime E. González; Andre A. Cire; Andrea Lodi; Louis-Martin Rousseau

    We propose an optimization framework which integrates decision diagrams (DDs) and integer linear programming (ILP) to solve combinatorial optimization problems. The hybrid DD-ILP approach explores the solution space based on a recursive compilation of relaxed DDs and incorporates ILP calls to solve subproblems associated with DD nodes. The selection of DD nodes to be explored by ILP technology is a

  • Constraints for generating graphs with imposed and forbidden patterns: an application to molecular graphs
    Constraints (IF 1.167) Pub Date : 2019-12-20
    Mohamed Amine Omrani; Wady Naanaa

    Although graphs are widely used to encode and solve various computational problems, little research exists on constrained graph construction. The current research was carried out to shed light on the problem of generating graphs, where the construction process is guided by various structural restrictions, like vertex degrees, proximity among vertices, and imposed and forbidden patterns. The main contribution

  • Incentive-based search for equilibria in boolean games
    Constraints (IF 1.167) Pub Date : 2019-08-20
    Vadim Levit; Zohar Komarovsky; Tal Grinshpoun; Ana L. C. Bazzan; Amnon Meisels

    Search for equilibria in games is a hard problem and many games do not have a pure Nash equilibrium (PNE). Incentive mechanisms have been shown to secure a PNE in certain families of games. The present study utilizes the similarity between Asymmetric Distributed Constraints Optimization Problems (ADCOPs) and games, to construct search algorithms for finding outcomes and incentives that secure a pure

  • Constraint Games for stable and optimal allocation of demands in SDN
    Constraints (IF 1.167) Pub Date : 2019-08-07
    Anthony Palmieri; Arnaud Lallouet; Luc Pons

    Software Defined Networking (or SDN) allows to apply a centralized control over a network of commuters in order to provide better global performances. One of the problem to solve is the multicommodity flow routing where a set of demands have to be routed at minimum cost. In contrast with other versions of this problem, we consider here problems with congestion that change the cost of a link according

  • Encoding cardinality constraints using multiway merge selection networks
    Constraints (IF 1.167) Pub Date : 2019-04-05
    Michał Karpiński; Marek Piotrów

    Boolean cardinality constraints (CCs) state that at most (at least, or exactly) k out of n propositional literals can be true. We propose a new, arc-consistent, easy to implement and efficient encoding of CCs based on a new class of selection networks. Several comparator networks have been recently proposed for encoding CCs and experiments have proved their efficiency (Abío et al. 2013, Asín et al

  • The item dependent stockingcost constraint
    Constraints (IF 1.167) Pub Date : 2019-02-19
    Vinasetan Ratheil Houndji; Pierre Schaus; Laurence Wolsey

    In a previous work we introduced a global StockingCost constraint to compute the total number of periods between the production periods and the due dates in a multi-order capacitated lot-sizing problem. Here we consider a more general case in which each order can have a different per period stocking cost and the goal is to minimise the total stocking cost. In addition the production capacity, limiting

  • Model counting with error-correcting codes
    Constraints (IF 1.167) Pub Date : 2019-02-08
    Dimitris Achlioptas; Panos Theodoropoulos

    The idea of counting the number of satisfying truth assignments (models) of a formula by adding random parity constraints can be traced back to the seminal work of Valiant and Vazirani showing that NP is as easy as detecting unique solutions. While theoretically sound, the random parity constraints used in that construction suffer from the following drawback: each constraint, on average, involves half

  • N -level Modulo-Based CNF encodings of Pseudo-Boolean constraints for MaxSAT
    Constraints (IF 1.167) Pub Date : 2019-01-12
    Aolong Zha; Miyuki Koshimura; Hiroshi Fujita

    Many combinatorial problems in various fields can be translated to Maximum Satisfiability (MaxSAT) problems. Although the general problem is \(\mathcal {N}\mathcal {P}\)-hard, more and more practical problems may be solved due to the significant effort which has been devoted to the development of efficient solvers. The art of constraints encoding is as important as the art of devising algorithms for

  • Not all FPRASs are equal: demystifying FPRASs for DNF-counting
    Constraints (IF 1.167) Pub Date : 2018-12-26
    Kuldeep S. Meel; Aditya A. Shrotri; Moshe Y. Vardi

    The problem of counting the number of solutions of a DNF formula, also called #DNF, is a fundamental problem in artificial intelligence with applications in diverse domains ranging from network reliability to probabilistic databases. Owing to the intractability of the exact variant, efforts have focused on the design of approximate techniques for #DNF. Consequently, several Fully Polynomial Randomized

  • Neighborhood singleton consistencies
    Constraints (IF 1.167) Pub Date : 2018-11-23
    Kostas Stergiou

    CP solvers predominantly use arc consistency (AC) as the default propagation method for binary constraints. Many stronger consistencies, such as triangle consistencies (e.g. RPC and maxRPC) exist, but their use is limited despite results showing that they outperform AC on many problems. This is due to the intricacies involved in incorporating them into solvers. On the other hand, singleton consistencies

  • Compiling CP subproblems to MDDs and d-DNNFs
    Constraints (IF 1.167) Pub Date : 2018-11-09
    Diego de Uña; Graeme Gange; Peter Schachte; Peter J. Stuckey

    Modeling discrete optimization problems is not straightforward. It is often the case that precompiling a subproblem that involves only a few tightly constrained variables as a table constraint can improve solving time. Nevertheless, enumerating all the solutions of a subproblem into a table can be costly in time and space. In this work we propose using Multivalued Decision Diagrams (MDDs) and formulas

  • Improved WPM encoding for coalition structure generation under MC-nets
    Constraints (IF 1.167) Pub Date : 2018-09-11
    Xiaojuan Liao; Miyuki Koshimura; Kazuki Nomoto; Suguru Ueda; Yuko Sakurai; Makoto Yokoo

    The Coalition Structure Generation (CSG) problem plays an important role in the domain of coalition games. Its goal is to create coalitions of agents so that the global welfare is maximized. To date, Weighted Partial MaxSAT (WPM) encoding has shown high efficiency in solving the CSG problem, which encodes a set of constraints into Boolean propositional logic and employs an off-the-shelf WPM solver

  • Constraints for symmetry breaking in graph representation
    Constraints (IF 1.167) Pub Date : 2018-08-25
    Michael Codish; Alice Miller; Patrick Prosser; Peter J. Stuckey

    Many complex combinatorial problems arising from a range of scientific applications (such as computer networks, mathematical chemistry and bioinformatics) involve searching for an undirected graph satisfying a given property. Since for any possible solution there can be a large number of isomorphic representations, these problems can quickly become intractable. One way to mitigate this problem is to

  • From MDD to BDD and Arc consistency
    Constraints (IF 1.167) Pub Date : 2018-07-12
    Julien Vion; Sylvain Piechowiak

    In this paper, we present a new conversion of multivalued decision diagrams (MDD) to binary decision diagrams (BDD) which can be used to improve MDD-based fil- tering algorithms such as MDDC or MDD-4R. We also propose BDDF, an algorithm that copies modified parts of the BDD “on the fly” during the search of a solution, and yields a better incrementality than a pure MDDC-like approach. MDDC is not very

  • MiniBrass: Soft constraints for MiniZinc
    Constraints (IF 1.167) Pub Date : 2018-07-05
    Alexander Schiendorfer; Alexander Knapp; Gerrit Anders; Wolfgang Reif

    Over-constrained problems are ubiquitous in real-world decision and optimization problems. Plenty of modeling formalisms for various problem domains involving soft constraints have been proposed, such as weighted, fuzzy, or probabilistic constraints. All of them were shown to be instances of algebraic structures. In terms of modeling languages, however, the field of soft constraints lags behind the

  • Translation-based approaches for solving disjunctive temporal problems with preferences
    Constraints (IF 1.167) Pub Date : 2018-07-02
    Enrico Giunchiglia; Marco Maratea; Luca Pulina

    Disjunctive Temporal Problems (DTPs) with Preferences (DTPPs) extend DTPs with piece-wise constant preference functions associated to each constraint of the form l ≤ x − y ≤ u, where x,y are (real or integer) variables, and l,u are numeric constants. The goal is to find an assignment to the variables of the problem that maximizes the sum of the preference values of satisfied DTP constraints, where

  • On a new extension of BTP for binary CSPs
    Constraints (IF 1.167) Pub Date : 2018-06-30
    Achref El Mouelhi

    The study of broken-triangles is becoming increasingly ambitious, by both solving constraint satisfaction problems (CSPs) in polynomial time and reducing search space size through either value merging or variable elimination. Considerable progress has been made in extending this important concept, such as dual broken-triangle and weakly broken-triangle, in order to maximize the number of captured tractable

  • Cost-based filtering algorithms for a Capacitated Lot Sizing Problem and the Constrained Arborescence Problem
    Constraints (IF 1.167) Pub Date : 2018-06-16
    Vinasetan Ratheil Houndji

    Constraint Programming (CP) is a paradigm derived from artificial intelligence, operational research, and algorithmics that can be used to solve combinatorial problems. CP solves problems by interleaving search (assign a value to an unassigned variable) and propagation. Constraint propagation aims at removing/filtering inconsistent values from the domains of the variables in order to reduce the search

  • Intruder alert! Optimization models for solving the mobile robot graph-clear problem
    Constraints (IF 1.167) Pub Date : 2018-05-21
    Michael Morin; Margarita P. Castro; Kyle E. C. Booth; Tony T. Tran; Chang Liu; J. Christopher Beck

    We develop optimization approaches to the graph-clear problem, a pursuit-evasion problem where mobile robots must clear a facility of intruders. The objective is to minimize the number of robots required. We contribute new formal results on progressive and contiguous assumptions and their impact on algorithm completeness. We present mixed-integer linear programming and constraint programming models

  • Online over time processing of combinatorial problems
    Constraints (IF 1.167) Pub Date : 2018-05-04
    Robinson Duque; Alejandro Arbelaez; Juan F. Díaz

    In an online environment, jobs arrive over time and there is no information in advance about how many jobs are going to be processed and what their processing times are going to be. In this paper, we study the online scheduling of Boolean Satisfiability (SAT) and Mixed Integer Programming (MIP) instances that are well-known NP-complete problems. Typical online machine scheduling approaches assume that

  • Deep neural networks and mixed integer linear optimization
    Constraints (IF 1.167) Pub Date : 2018-04-26
    Matteo Fischetti; Jason Jo

    Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made up of layers of internal units (or neurons), each of which computes an affine combination of the output of the units in the previous layer, applies a nonlinear operator, and outputs the corresponding value (also known as activation). A commonly-used nonlinear operator is the so-called

  • Linear-time filtering algorithms for the disjunctive constraint and a quadratic filtering algorithm for the cumulative not-first not-last
    Constraints (IF 1.167) Pub Date : 2018-04-11
    Hamed Fahimi; Yanick Ouellet; Claude-Guy Quimper

    We present new filtering algorithms for Disjunctive and Cumulative constraints, each of which improves the complexity of the state-of-the-art algorithms by a factor of log n. We show how to perform Time-Tabling and Detectable Precedences in linear time on the Disjunctive constraint. Furthermore, we present a linear-time Overload Checking for the Disjunctive and Cumulative constraints. Finally, we show

  • Modeling uncertainties with chance constraints
    Constraints (IF 1.167) Pub Date : 2018-04-03
    Imen Zghidi; Brahim Hnich; Abdelwaheb Rebaï

    Chance constraints are a major modeling tool for problems under uncertainty. We summarize the basic modeling ingredients of uncertain combinatorial problems and show how the Stochastic Constraint Satisfaction Problems formalism is able to support high-level declarative constructs that allow for ease of modeling of such problems in general. Then, we outline the different propagation methods for chance

  • IBM ILOG CP optimizer for scheduling
    Constraints (IF 1.167) Pub Date : 2018-03-19
    Philippe Laborie; Jérôme Rogerie; Paul Shaw; Petr Vilím

    IBM ILOG CP Optimizer is a generic CP-based system to model and solve scheduling problems. It provides an algebraic language with simple mathematical concepts to capture the temporal dimension of scheduling problems in a combinatorial optimization framework. CP Optimizer implements a model-and-run paradigm that vastly reduces the burden on the user to understand CP or scheduling algorithms: modeling

  • Constraint programming and operations research
    Constraints (IF 1.167) Pub Date : 2017-12-29
    J. N. Hooker; W.-J. van Hoeve

    We present an overview of the integration of constraint programming (CP) and operations research (OR) to solve combinatorial optimization problems. We interpret CP and OR as relying on a common primal-dual solution approach that provides the basis for integration using four main strategies. The first strategy tightly interweaves propagation from CP and relaxation from OR in a single solver. The second

  • Deriving generic bounds for time-series constraints based on regular expressions characteristics
    Constraints (IF 1.167) Pub Date : 2017-12-23
    Ekaterina Arafailova; Nicolas Beldiceanu; Helmut Simonis

    We introduce the concept of regular expression characteristics as a unified way to concisely express bounds on time-series constraints. This allows us not only to define time-series constraints in a compositional way, but also to deal with their combinatorial aspect in a compositional way, without developing ad-hoc bounds for each time-series constraint separately.

  • Mixed model line balancing with parallel stations, zoning constraints, and ergonomics
    Constraints (IF 1.167) Pub Date : 2017-11-29
    Anas Alghazi; Mary E. Kurz

    Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In

  • Improved filtering for the bin-packing with cardinality constraint
    Constraints (IF 1.167) Pub Date : 2017-10-14
    Guillaume Derval; Jean-Charles Régin; Pierre Schaus

    Previous research shows that a cardinality reasoning can improve the pruning of the bin-packing constraint. We first introduce a new algorithm, called BPCFlow, that filters both load and cardinality bounds on the bins, using a flow reasoning similar to the Global Cardinality Constraint. Moreover, we detect impossible assignments of items by combining the load and cardinality of the bins, using a method

  • How efficient is a global constraint in practice?
    Constraints (IF 1.167) Pub Date : 2017-10-13
    Sascha Van Cauwelaert; Michele Lombardi; Pierre Schaus

    Propagation is at the very core of t can provide signi: it can provide significant performance boosts as long as the search space reduction is not outweighed by the cost for running the propagators. A lot of research effort in the CP community is directed toward improving this trade-off. While experimental evaluation is here of the greatest importance, there exists no systematic and flexible methodology

  • Progress towards the Holy Grail
    Constraints (IF 1.167) Pub Date : 2017-10-07
    Eugene C. Freuder

    Twenty years ago in this journal, In Pursuit of the Holy Grail laid out a strategic vision for constraint programming. Much progress has been made towards the ideal posited in that paper. Many challenges and opportunities remain.

  • Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs
    Constraints (IF 1.167) Pub Date : 2017-08-18
    Ferdinando Fioretto; Enrico Pontelli; William Yeoh; Rina Dechter

    Discrete optimization is a central problem in artificial intelligence. The optimization of the aggregated cost of a network of cost functions arises in a variety of problems including Weighted Constraint Programs (WCSPs), Distributed Constraint Optimization (DCOP), as well as optimization in stochastic variants such as the tasks of finding the most probable explanation (MPE) in belief networks. Inference-based

  • Mining Time-constrained Sequential Patterns with Constraint Programming
    Constraints (IF 1.167) Pub Date : 2017-06-07
    John O. R. Aoga; Tias Guns; Pierre Schaus

    Constraint Programming (CP) has proven to be an effective platform for constraint based sequence mining. Previous work has focused on standard frequent sequence mining, as well as frequent sequence mining with a maximum ’gap’ between two matching events in a sequence. The main challenge in the latter is that this constraint can not be imposed independently of the omnipresent frequency constraint. Indeed

  • Auto-tabling for subproblem presolving in MiniZinc
    Constraints (IF 1.167) Pub Date : 2017-06-06
    Jip J. Dekker; Gustav Björdal; Mats Carlsson; Pierre Flener; Jean-Noël Monette

    A well-known and powerful constraint model reformulation is to compute the solutions to a model part, say a custom constraint predicate, and tabulate them within an extensional constraint that replaces that model part. Despite the possibility of achieving higher solving performance, this tabling reformulation is often not tried, because it is tedious to perform; further, if successful, it obfuscates

  • A global constraint for over-approximation of real-time streams
    Constraints (IF 1.167) Pub Date : 2017-05-31
    Anicet Bart; Charlotte Truchet; Eric Monfroy

    Formal verification of real time programs, where variables can change values at every time step, is difficult due to the analyses of loops with time lags. In this paper, we propose a constraint programming model together with a global constraint and a filtering algorithm, for computing over-approximation of real-time streams. The global constraint handles the loop analyses by providing an interval

  • Cumulative scheduling with variable task profiles and concave piecewise linear processing rate functions
    Constraints (IF 1.167) Pub Date : 2017-05-23
    Margaux Nattaf; Christian Artigues; Pierre Lopez

    We consider a cumulative scheduling problem where a task duration and resource consumption are not fixed. The consumption profile of the task, which can vary continuously over time, is a decision variable of the problem to be determined and a task is completed as soon as the integration over its time window of a non-decreasing and continuous processing rate function of the consumption profile has reached

  • Efficient filtering for the Resource-Cost AllDifferent constraint
    Constraints (IF 1.167) Pub Date : 2017-05-23
    Sascha Van Cauwelaert; Pierre Schaus

    This paper studies a family of optimization problems where a set of items, each requiring a possibly different amount of resource, must be assigned to different slots for which the price of the resource can vary. The objective is then to assign items such that the overall resource cost is minimized. Such problems arise commonly in domains such as production scheduling in the presence of fluctuating

  • Using constraint programming for solving RCPSP/max-cal
    Constraints (IF 1.167) Pub Date : 2017-01-17
    Stefan Kreter; Andreas Schutt; Peter J. Stuckey

    Resource-constrained project scheduling with the objective of minimizing project duration (RCPSP) is one of the most studied scheduling problems. In this paper we consider the RCPSP with general temporal constraints and calendar constraints. Calendar constraints make some resources unavailable on certain days in the scheduling period and force activity execution to be delayed while resources are unavailable

  • Domain reduction techniques for global NLP and MINLP optimization
    Constraints (IF 1.167) Pub Date : 2017-01-14
    Yash Puranik; Nikolaos V. Sahinidis

    Optimization solvers routinely utilize presolve techniques, including model simplification, reformulation and domain reduction techniques. Domain reduction techniques are especially important in speeding up convergence to the global optimum for challenging nonconvex nonlinear programming (NLP) and mixed-integer nonlinear programming (MINLP) optimization problems. In this work, we survey the various

  • Towards statistical consistency for stochastic constraint programming
    Constraints (IF 1.167) Pub Date : 2017-01-06
    Imen Zghidi

    In most industrial contexts, decisions are made based on incomplete information. This is due to the fact that decision makers cannot be certain of the future behavior of factors that will affect the outcome resulting from various options under consideration. Stochastic Constraint Satisfaction Problems provide a powerful modeling framework for problems in which one is required to take decisions under

  • What is answer set programming to propositional satisfiability
    Constraints (IF 1.167) Pub Date : 2016-12-16
    Yuliya Lierler

    Propositional satisfiability (or satisfiability) and answer set programming are two closely related subareas of Artificial Intelligence that are used to model and solve difficult combinatorial search problems. Satisfiability solvers and answer set solvers are the software systems that find satisfying interpretations and answer sets for given propositional formulas and logic programs, respectively.

  • Combining techniques of bounded model checking and constraint programming to aid for error localization
    Constraints (IF 1.167) Pub Date : 2016-12-15
    Mohammed Bekkouche

    A model checker can produce a trace of counter-example for erroneous program, which is often difficult to exploit to locate errors in source code. In my thesis, we proposed an error localization algorithm from counter-examples, named LocFaults, combining approaches of Bounded Model-Checking (BMC) with constraint satisfaction problem (CSP). This algorithm analyzes the paths of CFG (Control Flow Graph)

  • Constraint-directed search for all-interval series
    Constraints (IF 1.167) Pub Date : 2016-12-15
    Md Masbaul Alam Polash; M. A. Hakim Newton; Abdul Sattar

    All-interval series is a standard benchmark problem for constraint satisfaction search. An all-interval series of size n is a permutation of integers [0, n) such that the differences between adjacent integers are a permutation of [1, n). Generating each such all-interval series of size n is an interesting challenge for constraint community. The problem is very difficult in terms of the size of the

  • Extensible automated constraint modelling via refinement of abstract problem specifications
    Constraints (IF 1.167) Pub Date : 2016-12-15
    Özgür Akgün

    Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial (optimisation) problems. Constraint solving a given problem proceeds in two phases: modelling and solving. Effective modelling has an huge impact on the performance of the solving process. This thesis presents a framework in which the users are not required to make modelling decisions, concrete CP models are automatically

  • Other things besides number: Abstraction, constraint propagation, and string variable types
    Constraints (IF 1.167) Pub Date : 2016-12-12
    Joseph Scott

    In constraint programming (CP), a combinatorial problem is modeled declaratively as a conjunction of constraints, each of which captures some of the combinatorial substructure of the problem. Constraints are more than a modeling convenience: every constraint is partially implemented by an inference algorithm, called a propagator, that rules out some but not necessarily all infeasible candidate values

  • Beyond the structure of SAT formulas
    Constraints (IF 1.167) Pub Date : 2016-12-06
    Jesús Giráldez-Cru

    Nowadays, many real-world problems are encoded into SAT instances and efficiently solved by modern SAT solvers. These solvers, usually known as Conflict-Driven Clause Learning (CDCL) SAT solvers, include a variety of sophisticated techniques, such as clause learning, lazy data structures, conflict-based adaptive branching heuristics, or random restarts, among others. However, the reasons of their efficiency

  • Tractable classes for CSPs of arbitrary arity: from theory to practice
    Constraints (IF 1.167) Pub Date : 2016-12-06
    Achref El Mouelhi

    The research of this thesis focuses on the analysis of polynomial classes and their practical exploitation for solving constraint satisfaction problems (CSPs) with finite domains. In particular, I worked on bridging the gap between theoretical works and practical results in constraint solvers. Specifically, the goal of this thesis is to find explanation for the effectiveness of solvers, and also to

  • Revisiting restricted path consistency
    Constraints (IF 1.167) Pub Date : 2016-09-14
    Kostas Stergiou

    Restricted path consistency (RPC) is a strong local consistency for binary constraints that was proposed 20 years ago and was identified as a promising alternative to arc consistency (AC) in an early experimental study of local consistencies for binary constraints. However, in contrast to other strong local consistencies such as singleton arc consistency (SAC) and max restricted path consistency (maxRPC)

  • Graphical models for optimal power flow
    Constraints (IF 1.167) Pub Date : 2016-09-13
    Krishnamurthy Dvijotham; Michael Chertkov; Pascal Van Hentenryck; Marc Vuffray; Sidhant Misra

    Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables

  • Constraint programming for planning test campaigns of communications satellites
    Constraints (IF 1.167) Pub Date : 2016-09-13
    Emmanuel Hebrard; Marie-José Huguet; Daniel Veysseire; Ludivine Boche Sauvan; Bertrand Cabon

    The payload of communications satellites must go through a series of tests to assert their ability to survive in space. Each test involves some equipment of the payload to be active, which has an impact on the temperature of the payload. Sequencing these tests in a way that ensures the thermal stability of the payload and minimizes the overall duration of the test campaign is a very important objective

  • Triangle-based consistencies for cost function networks
    Constraints (IF 1.167) Pub Date : 2016-09-03
    Hiep Nguyen; Christian Bessiere; Simon de Givry; Thomas Schiex

    Cost Function Networks (aka Weighted CSP) allow to model a variety of problems, such as optimization of deterministic and stochastic graphical models including Markov random Fields and Bayesian Networks. Solving cost function networks is thus an important problem for deterministic and probabilistic reasoning. This paper focuses on local consistencies which define essential tools to simplify Cost Function

  • Prefix-projection global constraint and top- k approach for sequential pattern mining
    Constraints (IF 1.167) Pub Date : 2016-08-26
    Amina Kemmar; Yahia Lebbah; Samir Loudni; Patrice Boizumault; Thierry Charnois

    Sequential pattern mining (SPM) is an important data mining problem with broad applications. SPM is a hard problem due to the huge number of intermediate subsequences to be considered. State of the art approaches for SPM (e.g., PrefixSpan Pei et al. 2001) are largely based on the pattern-growth approach, where for each frequent prefix subsequence, only its related suffix subsequences need to be considered

  • The power of propagation: when GAC is enough
    Constraints (IF 1.167) Pub Date : 2016-08-23
    David A. Cohen; Peter G. Jeavons

    Considerable effort in constraint programming has focused on the development of efficient propagators for individual constraints. In this paper, we consider the combined power of such propagators when applied to collections of more than one constraint. In particular we identify classes of constraint problems where such propagators can decide the existence of a solution on their own, without the need

  • “Almost-stable” matchings in the Hospitals / Residents problem with Couples
    Constraints (IF 1.167) Pub Date : 2016-08-11
    David F. Manlove; Iain McBride; James Trimble

    The Hospitals / Residents problem with Couples (hrc) models the allocation of intending junior doctors to hospitals where couples are allowed to submit joint preference lists over pairs of (typically geographically close) hospitals. It is known that a stable matching need not exist, so we consider min bp hrc, the problem of finding a matching that admits the minimum number of blocking pairs (i.e.,

  • Combining restarts, nogoods and bag-connected decompositions for solving CSPs
    Constraints (IF 1.167) Pub Date : 2016-08-02
    Philippe Jégou; Cyril Terrioux

    From a theoretical viewpoint, the (tree-)decomposition methods offer a good approach for solving Constraint Satisfaction Problems (CSPs) when their (tree)-width is small. In this case, they have often shown their practical interest. So, the literature (coming from Mathematics, OR or AI) has concentrated its efforts on the minimization of a single parameter, namely the tree-width. Nevertheless, experimental

  • Multi-language evaluation of exact solvers in graphical model discrete optimization
    Constraints (IF 1.167) Pub Date : 2016-04-22
    Barry Hurley; Barry O’Sullivan; David Allouche; George Katsirelos; Thomas Schiex; Matthias Zytnicki; Simon de Givry

    By representing the constraints and objective function in factorized form, graphical models can concisely define various NP-hard optimization problems. They are therefore extensively used in several areas of computer science and artificial intelligence. Graphical models can be deterministic or stochastic, optimize a sum or product of local functions, defining a joint cost or probability distribution

Contents have been reproduced by permission of the publishers.
Springer 纳米技术权威期刊征稿
ACS ES&T Engineering
ACS ES&T Water
ACS Publications填问卷