By Althaus E.

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9; 1 7 16. 4; 1 12 7:51. 5; 1 18 29:42. 0; 10 50 7:49. 6; 1 14 9:24. 1; 1 27 40:38. 0; 1 14 2:08. 0]; 1 51 10:02:04. 4; 1 16 6:55. 7]; 1 45 12:33:06. 3]; 1 34 10:08:14. 1]; 1 120 10:23:22. 4]; 9 149 10:06:15. 8]; 1 109 10:28:33. 0]; 1 92 10:30:51. 0]; 1 30 10:37:44. 3]; 1 31 10:00:06. 3. Comparison with other programs We compared our algorithm with the most recent structural alignments algorithms, considering the overall best performing programs from the survey [27]: PRRP, ClustalX, and Dialign, together with a recently published program T-Coffee [22], which generally outperforms the other programs.

The value of δ is set to 25 for the instances in our test bed, in order to ensure that that the optimal alignment for the resulting graph can easily be computed. Furthermore, we implemented the following LP-based primal heuristic that is applied at each LP iteration. We sort the edge variables according to decreasing LP values. Then, we iterate over the edges, and include each of them in the solution if its addition does not create a mixed cycle. Finally we add the edges transitively implied by the included ones.

In general, the initial heuristic, which is also based on our branch-and-cut approach, was capable of producing solutions of very good quality (for none of the instances for which the time limit was reached a better solution was found). 422 E. Althaus et al. Table 3. 8; 1 13 3:14. 9; 1 5 2. 9; 19 65 7:08. 9; 1 11 5:15. 3; 1 6 2. 6; 1 14 17. 0; 1 10 34. 8; 1 6 4. 7; 1 17 30. 0; 1 10 20. 9; 1 7 16. 4; 1 12 7:51. 5; 1 18 29:42. 0; 10 50 7:49. 6; 1 14 9:24. 1; 1 27 40:38. 0; 1 14 2:08. 0]; 1 51 10:02:04.

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