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Extra info for A branch-and-cut algorithm for multiple sequence alignment

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1aab (4/291) 1fjlA (6/398) 1hfh (5/606) 1hpi (4/293) 1csy (5/510) 1pfc (5/560) 1tgxA (4/239) 1ycc (4/426) 3cyr (4/414) 451c (5/400) avg. 1aboA (5/297) 1idy (5/269) 1r69 (4/277) 1tvxA (4/242) 1ubi (4/327) 1wit (5/484) 2trx (4/362) avg. 282 (51) of eliminating a wide set of useful variables. Currently, our method is not competitive for these instances. 5. Conclusions and further work A comparison of the known multiple sequence alignment tools shows that almost all of these programs are heuristics that try to find good approximations of the optimal multiple alignment.

1, at least for the first LPs. However, when these heuristics were used, both the number of LPs to be solved and the total running time increased (keep in mind that the time for exact separation was small). Therefore, we apply only exact separation in our implementation. In order to test the effectiveness of the three classes of inequalities (8) (with gap variables, since those without gap variables are represented in compact form), (9) and (10), we tried not to separate some of them and observed the outcome.

For the easy instances, reoptimizing the LPs with the dual simplex algorithm was slightly faster than solving them from scratch with the barrier method. On the other hand, contrary to what is generally observed in other cases, for the challenging instances, the barrier method widely outperformed the simplex algorithm, provided one resigned to crossover to a basic solution. ) Table 1 shows the total running times to solve the eight instances of the group V2 for which our method found an optimal solution, where, for the dual simplex algorithm, results are reported for the two pricing options of CPLEX (automatic and steepest descent).

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A branch-and-cut algorithm for multiple sequence alignment by Althaus E.

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