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Second Year Viva. Chris Unsworth. What I have done. A new description for SMN. Why? To fit more intuitive into an AC5 style arc-consistency algorithm. As a blueprint for future constraint discriptions. A new complexity argument to fit the new description. Proofs
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Second Year Viva Chris Unsworth
What I have done • A new description for SMN. • Why? • To fit more intuitive into an AC5 style arc-consistency algorithm. • As a blueprint for future constraint discriptions. • A new complexity argument to fit the new description. • Proofs • soundness and completeness • Equivalence to the EGS algorithm • Failure free enumeration of all stable matchings
What I have done • Empirical study of constraint solutions to the stable marriage problem. • Solutions included: • Conflict matrix encoding • Boolean encoding • N-Valued encoding • 4-Valued encoding • SM2 constraint • SMN constraint • EGS algorithm • Instance sizes and sample sizes • 10 – 100 sample size 1000 • 100 – 1000 sample size 1000 • 1000 – 8000 sample size 20 • SMN is comparable to EGS • when n >= 1000, EGS at most 4 times faster than EGS
What I have done • Comparing the average number of solutions lead the conjecture that: “after arc-consistency has been enforced over a constraint model which uses the SMN constraint, the first stable matching can be found in n time and each subsequent solution can also be found in n time”
What I am doing • Starting 17th October 2005 • Check implementation of all CP SM models, including: • FT, Bool, DG1, DG2, SM2 and SMN • Get a reasonable implementation of the EGS algorithm in Java • Design, run and tabulate an empirical study of the above models • Starting November 2005 • Check implementation of SMTI2 • Implement SMTIN • Recreate ECAI 02 experiments and include the new models • Increase the instance sizes to explore the limits of the new solutions • Write up the work with a view to submitting to ECAI 06 (deadline for summaries 8th February 2006)
What I am going to do • Provisional thesis statement • “A Specialised constraint solution for a stable matching problem can solve the problem in comparable time to an algorithmic solution, and it can significantly outperform a more traditional toolbox constraint solution whilst maintaining its versatility ”