Abstract
Russian Doll Search (RDS) is a clever procedure to solve overconstrained problems. RDS solves a sequence of nested subproblems, where two consecutive subproblems differ in one variable only. We present the Specialized RDS (SRDS) algorithm, which solves the current subproblem for each value of the new variable with respect to the previous subproblem. The SRDS lower bound is superior to the RDS lower bound, which allows for a higher level of value pruning, although more work per node is required. Experimental results on random and real problems show that this extra work is often beneficial, providing substantial savings in the global computational effort.
This work was supported by the IST Programme of the Commission of the European Union through the ECSPLAIN project (IST-1999-11969), and by the Spanish CICYT project TAP99-1086-C03-02.
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Meseguer, P., Sánchez, M. (2001). Specializing Russian Doll Search. In: Walsh, T. (eds) Principles and Practice of Constraint Programming — CP 2001. CP 2001. Lecture Notes in Computer Science, vol 2239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45578-7_32
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DOI: https://doi.org/10.1007/3-540-45578-7_32
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