| Abstract | Viterbi decoding is, however, prohibitively slow when the label set is large, because its time complexity is quadratic in the number of labels. |
| Introduction | The time complexity of the Viterbi algorithm is O(NL2) since there are NL nodes in the lattice and it takes 0(L) time to evaluate the score of each node. |
| Introduction | Staggered decoding has 0(N) best case time complexity and 0(NL2) worst case time complexity . |
| Introduction | Therefore, it has 0(23221 N4m_1) time complexity if it terminates after the M -th search step. |
| Conclusion | Given the fact that fanout l bundles can be attached to any adjacent bundle in our factorization, we can show that our algorithm also optimizes time complexity for known tabular parsing algorithms for LCFRSs with fanout 2. |
| Conclusion | As for general LCFRS, it has been shown by Gildea (2010) that rank optimization and time complexity optimization are not equivalent. |
| Conclusion | Furthermore, all known algorithms for rank or time complexity optimization have an exponential time complexity (Gomez-Rodriguez et al., 2009). |
| Time complexity analysis | In this section we sketch the proof of this result, which will prove the quadratic time complexity of our algorithm. |
| Time complexity analysis | Our induction hypothesis is that for m < n, the time complexity is in (9(m2). |