scholarly journals Exploiting Single-Cycle Symmetries in Continuous Constraint Problems

2009 ◽  
Vol 34 ◽  
pp. 499-520 ◽  
Author(s):  
V. Ruiz de Angulo ◽  
C. Torras
Keyword(s):  
Constraint Satisfaction ◽  
Simple Algorithm ◽  
Constraint Satisfaction Problems ◽  
Single Cycle ◽  
Constraint Solver ◽  
Dimensional Cube ◽  
Very High

Symmetries in discrete constraint satisfaction problems have been explored and exploited in the last years, but symmetries in continuous constraint problems have not received the same attention. Here we focus on permutations of the variables consisting of one single cycle. We propose a procedure that takes advantage of these symmetries by interacting with a continuous constraint solver without interfering with it. A key concept in this procedure are the classes of symmetric boxes formed by bisecting a n-dimensional cube at the same point in all dimensions at the same time. We analyze these classes and quantify them as a function of the cube dimensionality. Moreover, we propose a simple algorithm to generate the representatives of all these classes for any number of variables at very high rates. A problem example from the chemical field and the cyclic n-roots problem are used to show the performance of the approach in practice.

Reducing the Complexity of Modeling Large Software Systems

2009 ◽  
pp. 3399-3429
Author(s):  
Jules White ◽  
Douglas C. Schmidt ◽  
Andrey Nechypurenko ◽  
Egon Wuchner
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Correct Solution ◽  
Software Systems ◽  
Domain Expert ◽  
Modeling Tool ◽  
Constraint Solver ◽  
Model Driven ◽  
Model Driven Approach ◽  
Domain Constraints

Model-driven development is one approach to combating the complexity of designing software intensive systems. A model-driven approach allows designers to use domain notations to specify solutions and domain constraints to ensure that the proposed solutions meet the required objectives. Many domains, however, require models that are either so large or intricately constrained that it is extremely difficult to manually specify a correct solution. This chapter presents an approach to provide that leverages a constraint solver to provide modeling guidance to a domain expert. The chapter presents both a practical framework for transforming models into constraint satisfaction problems and shows how the Command Pattern can be used to integrate a constraint solver into a modeling tool.

scholarly journals Fast Set Bounds Propagation Using a BDD-SAT Hybrid

2010 ◽  
Vol 38 ◽  
pp. 307-338 ◽  
Author(s):  
G. Gange ◽  
P. J. Stuckey ◽  
V. Lagoon
Keyword(s):  
Constraint Satisfaction ◽  
Binary Decision Diagram ◽  
Constraint Satisfaction Problems ◽  
Basic Algorithm ◽  
Learning Abilities ◽  
Binary Decision ◽  
Constraint Solver ◽  
Powerful Approach ◽  
Sat Solver

Binary Decision Diagram (BDD) based set bounds propagation is a powerful approach to solving set-constraint satisfaction problems. However, prior BDD based techniques in- cur the significant overhead of constructing and manipulating graphs during search. We present a set-constraint solver which combines BDD-based set-bounds propagators with the learning abilities of a modern SAT solver. Together with a number of improvements beyond the basic algorithm, this solver is highly competitive with existing propagation based set constraint solvers.

scholarly journals Search Strategies as Synchronous Processes (Extended Abstract)

2017 ◽  
Author(s):  
Pierre Talbot
Keyword(s):  
Constraint Satisfaction ◽  
Search Strategy ◽  
State Of The Art ◽  
Search Strategies ◽  
Constraint Satisfaction Problems ◽  
Constraint Solver ◽  
Process Calculi

Solving constraint satisfaction problems (CSP) efficiently depends on the solver configuration and the search strategy. However, it is difficult to customize the constraint solvers because they are not modular enough, and it is hard to create new search strategies by composition. To solve these problems, we propose spacetime programming, a paradigm based on lattices and synchronous process calculi that views search strategies as processes working collaboratively towards the resolution of a CSP. We implement the compiler of the language and use it to replace the search module of Choco, a state of the art constraint solver, with an efficient spacetime program that offers better modularity and compositionality of search strategies.

Reducing the Complexity of Modeling Large Software Systems

2009 ◽  
pp. 372-400
Author(s):  
Jules White ◽  
Douglas C. Schmidt ◽  
Andrey Nechypurenko ◽  
Egon Wuchner
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Correct Solution ◽  
Software Systems ◽  
Domain Expert ◽  
Modeling Tool ◽  
Constraint Solver ◽  
Model Driven ◽  
Model Driven Approach ◽  
Domain Constraints

Model-driven development is one approach to combating the complexity of designing software intensive systems. A model-driven approach allows designers to use domain notations to specify solutions and domain constraints to ensure that the proposed solutions meet the required objectives. Many domains, however, require models that are either so large or intricately constrained that it is extremely difficult to manually specify a correct solution. This chapter presents an approach to provide that leverages a constraint solver to provide modeling guidance to a domain expert. The chapter presents both a practical framework for transforming models into constraint satisfaction problems and shows how the Command Pattern can be used to integrate a constraint solver into a modeling tool.

scholarly journals Intelligent search strategies based on adaptive Constraint Handling Rules

2005 ◽  
Vol 5 (4-5) ◽  
pp. 567-594 ◽  
Author(s):  
ARMIN WOLF
Keyword(s):  
Constraint Satisfaction ◽  
Search Strategies ◽  
Constraint Satisfaction Problems ◽  
Constraint Handling ◽  
Search Process ◽  
Constraint Solver ◽  
Intelligent Search ◽  
Constraint Processing ◽  
Made In

The most advanced implementation of adaptive constraint processing with Constraint Handling Rules (CHR) allows the application of intelligent search strategies to solve Constraint Satisfaction Problems (CSP). This presentation compares an improved version of conflict-directed backjumping and two variants of dynamic backtracking with respect to chronological backtracking on some of the AIM instances which are a benchmark set of random 3-SAT problems. A CHR implementation of a Boolean constraint solver combined with these different search strategies in Java is thus being compared with a CHR implementation of the same Boolean constraint solver combined with chronological backtracking in SICStus Prolog. This comparison shows that the addition of “intelligence” to the search process may reduce the number of search steps dramatically. Furthermore, the runtime of their Java implementations is in most cases faster than the implementations of chronological backtracking. More specifically, conflict-directed backjumping is even faster than the SICStus Prolog implementation of chronological backtracking, although our Java implementation of CHR lacks the optimisations made in the SICStus Prolog system.

scholarly journals Finding robust solutions for constraint satisfaction problems with discrete and ordered domains by coverings

2013 ◽  
Vol 44 (2) ◽  
pp. 131-156 ◽  
Author(s):  
Laura Climent ◽  
Richard J. Wallace ◽  
Miguel A. Salido ◽  
Federico Barber
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Robust Solutions

scholarly journals A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems

2011 ◽  
Vol 21 (4) ◽  
pp. 733-744 ◽  
Author(s):  
Marlene Arangú ◽  
Miguel Salido
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Programming ◽  
Real Life ◽  
Search Space ◽  
Constraint Satisfaction Problems ◽  
Fine Grained ◽  
Arc Consistency ◽  
Life Problems ◽  
Programming Techniques ◽  
Np Complete

A fine-grained arc-consistency algorithm for non-normalized constraint satisfaction problems Constraint programming is a powerful software technology for solving numerous real-life problems. Many of these problems can be modeled as Constraint Satisfaction Problems (CSPs) and solved using constraint programming techniques. However, solving a CSP is NP-complete so filtering techniques to reduce the search space are still necessary. Arc-consistency algorithms are widely used to prune the search space. The concept of arc-consistency is bidirectional, i.e., it must be ensured in both directions of the constraint (direct and inverse constraints). Two of the most well-known and frequently used arc-consistency algorithms for filtering CSPs are AC3 and AC4. These algorithms repeatedly carry out revisions and require support checks for identifying and deleting all unsupported values from the domains. Nevertheless, many revisions are ineffective, i.e., they cannot delete any value and consume a lot of checks and time. In this paper, we present AC4-OP, an optimized version of AC4 that manages the binary and non-normalized constraints in only one direction, storing the inverse founded supports for their later evaluation. Thus, it reduces the propagation phase avoiding unnecessary or ineffective checking. The use of AC4-OP reduces the number of constraint checks by 50% while pruning the same search space as AC4. The evaluation section shows the improvement of AC4-OP over AC4, AC6 and AC7 in random and non-normalized instances.

Sign in / Sign up

Export Citation Format

Share Document