A Comparison of CP, IP and Hybrids for Configuration Problems

We investigate different solution techniques for solving a basic part of configuration problems, namely linear arithmetic constraints over integer variables. Approaches include integer programming, constraint programming over finite domains and hybrid techniques. We also discuss important extensions of the basic problem and how these can be accommodated in the different solution approaches

Worst Case Execution Time Analysis for Modern Hardware Architectures

Knowing the worst case execution times #WCETs# for programs are crucial for the design and veri#cation of real-time systems. Modern hardware architectures utilize pipelinedexecution and cache memory for improved performance. We extend an existing execution time analysis technique, the Implicit Path Enumeration Technique #IPET#, to consider these and other modern hardwarearchitecturefeatures. We extend IPET in two stages. First, we annotate the control #ow graph of the program with variables representing the history of execution, thus allowing the state of architectural entities, such as cache and pipeline, to be determined before each basic block. Secondly, we model the architectural entities with constraints. The result is an equation which contains a complete model of how the program will execute on the modeled architecture. This novel idea provides a straightforward and #exible way of incorporating the behavior of various modern hardwarearchitecturefeatures into WCET analysis

Integration of Constraint Programming and Integer Programming for Combinatorial Optimization

The last several years have seen an increasing interest in combining the models and methods of optimization with those of constraint programming. Integration of the two was initially impeded by their di erent cultural origins, one having developed largely in the operations research community and the other in the computer science and arti cial intelligence communities. The advantages of merger, however, are rapidly overcoming this barrier. The main objective for an integration of Constraint Programming over-nite domains (CP) and Integer Programming (IP) is to take advantage of both the inference through constraint propagation and the (continuous) relaxations through Linear Programming (LP), in order to reduce the search needed to nd feasible, good and optimal solutions. The key decisions to be made for integrating CP and IP are (a) the model(s), (b) the inference, (c) the relaxations, and, (d) the search and branching strategies to use. In this thesis it is advocated to model speci cally for

Anytime frequency allocation with soft constraints

As is well known, the frequency allocation problem for mobile telephone networks can be approximated by a graph coloring problem where vertices model transmitters, colors model frequencies, and an edge corresponds to a pair of transmitters that must not use the same frequency. However, the graph coloring analogy doesn't suffice for modeling real world problems, which involve soft constraints and general distance constraints. Thus, general constraint satisfaction techniques are called for. In this paper, we model the frequency allocation problem as a partial constraint satisfaction problem, and give a branch & bound algorithm for solving it. We review a number of constraint solving techniques used in this problem and discuss their relevance to performance

An Open-Ended finite domain constraint solver

We describe the design and implementation of a finite domain constraint solver embedded in a Prolog system using an extended unification mechanism via attributed variables as a generic constraint interface. The solver is essentially a scheduler for indexicals, i.e. reactive functional rules encoding local consistency methods performing incremental constraint solving or entailment checking, and global constraints, i.e. general propagators which may use specialized algorithms to achieve a higher degree of consistency or better time and space complexity. The solver has an open-ended design: the user can introduce new constraints, either in terms of indexicals by writing rules in a functional notation, or as global constraints via a Prolog programming interface. Constraints defined in terms of indexicals can be linked to 0/1-variables modeling entailment; thus indexicals are used for constraint solving as well as for entailment testing. Constraints can be arbitrarily combined using the ..

Linear Relaxations and Reduced-Cost Based Propagation of Continuous Variable Subscripts

In hybrid solvers for combinatorial optimization, combining Constraint (Logic) Programming (CLP) and Mixed Integer Programming (MIP), it is important to have tight connections between the two domains. We extend and generalize previous work on automatic linearizations and propagation of symbolic CLP constraints that cross the boundary between CLP and MIP. We also present how reduced costs from the linear programming relaxation can be used for domain reduction on the CLP side. Computational results comparing our hybrid approach with pure CLP and MIP on a configuration problem show significant speed-ups