A review of literature on parallel constraint solving
As multi-core computing is now standard, it seems irresponsible for constraints researchers
to ignore the implications of it. Researchers need to address a number of issues to exploit …
to ignore the implications of it. Researchers need to address a number of issues to exploit …
[HTML][HTML] Conjure: Automatic generation of constraint models from problem specifications
When solving a combinatorial problem, the formulation or model of the problem is critical to
the efficiency of the solver. Automating the modelling process has long been of interest …
the efficiency of the solver. Automating the modelling process has long been of interest …
[HTML][HTML] Automated streamliner portfolios for constraint satisfaction problems
Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial
problems. Solving a problem proceeds in two distinct phases: modelling and solving …
problems. Solving a problem proceeds in two distinct phases: modelling and solving …
A framework for generating informative benchmark instances
Benchmarking is an important tool for assessing the relative performance of alternative
solving approaches. However, the utility of benchmarking is limited by the quantity and …
solving approaches. However, the utility of benchmarking is limited by the quantity and …
Towards reformulating Essence specifications for robustness
The Essence language allows a user to specify a constraint problem at a level of abstraction
above that at which constraint modelling decisions are made. Essence specifications are …
above that at which constraint modelling decisions are made. Essence specifications are …
Closed frequent itemset mining with arbitrary side constraints
Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It
has several application areas, such as market basket analysis, genome analysis, and drug …
has several application areas, such as market basket analysis, genome analysis, and drug …
Automatic generation and selection of streamlined constraint models via monte carlo search on a model lattice
Streamlined constraint reasoning is the addition of uninferred constraints to a constraint
model to reduce the search space, while retaining at least one solution. Previously it has …
model to reduce the search space, while retaining at least one solution. Previously it has …
Exploiting incomparability in solution dominance: Improving general purpose constraint-based mining
In data mining, finding interesting patterns is a challenging task. Constraint-based mining is
a well-known approach to this, and one for which constraint programming has been shown …
a well-known approach to this, and one for which constraint programming has been shown …
Towards portfolios of streamlined constraint models: a case study with the balanced academic curriculum problem
Augmenting a base constraint model with additional constraints can strengthen the
inferences made by a solver and therefore reduce search effort. We focus on the automatic …
inferences made by a solver and therefore reduce search effort. We focus on the automatic …
Automatic streamlining for constrained optimisation
Augmenting a base constraint model with additional constraints can strengthen the
inferences made by a solver and therefore reduce search effort. We focus on the automatic …
inferences made by a solver and therefore reduce search effort. We focus on the automatic …