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Constraint Programming (CP) has been successfully applied to several combinatorial optimization problems. One of its advantages is the availability of complex global constraints performing efficient propagation and interacting with each other through shared variables. However, CP techniques have...
A common telecommunications network design strategy, in order to protect it against failures, is to achieve a decomposition into a set of rings covering the network. We propose a hybrid algorithm for the corresponding Constrained Cycle Cover Problem which draws from constraint programming and...
Benchmark comparisons tend to overlook the most important challenge in solving combinatorial problems: how to design an appropriate algorithm. For example, an early version of Localizer incurred a factor 3 performance penalty when benchmarked against a ‘C’ implementation of GSAT, but we would...
The complementing strengths of Constraint (Logic) Programming (CLP) and Mixed Integer Programming (IP) have recently received significant attention. Although various optimization and constraint programming packages at a first glance seem to support mixed models, the modeling and solution...
Two contrasting search paradigms for solving combinatorial problems are systematic backtracking and local search. The former is often effective on highly structured problems because of its ability to exploit consistency techniques, while the latter tends to scale better on very large problems....
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