# Easy and Hard Constraint Ranking in OT: Algorithms and Complexity

@article{Eisner2000EasyAH, title={Easy and Hard Constraint Ranking in OT: Algorithms and Complexity}, author={Jason Eisner}, journal={ArXiv}, year={2000}, volume={cs.CL/0102019} }

We consider the problem of ranking a set of OT constraints in a manner consistent with data.
We speed up Tesar and Smolensky's RCD algorithm to be linear on the number of constraints. This finds a ranking so each attested form x_i beats or ties a particular competitor y_i. We also generalize RCD so each x_i beats or ties all possible competitors.
Alas, this more realistic version of learning has no polynomial algorithm unless P=NP! Indeed, not even generation does. So one cannot improve…

## 4 Citations

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