Models are used in science and engineering for experimentation,
analysis, model-based diagnosis, design and planning/sheduling
applications. Many of these models are overconstrained Numeric Constraint
Satisfaction Problems (NCSP), where the numeric constraints
could have linear or polynomial relations. In practical scenarios, it is
very useful to know which parts of the overconstrained NCSP instances
cause the unsolvability.
Although there are algorithms to find all optimal solutions for this
problem, they are computationally expensive, and hence may not be applicable
to large and real-world problems. Our objective is to improve
the performance of these algorithms for numeric domains using structural
analysis. We provide experimental results showing that the use of
the different strategies proposed leads to a substantially improved performance
and it facilitates the application of solving larger and more
realistic problems.Ministerio de Educación y Ciencia DIP2006-15476-C02-0