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Exact(22)
From Eq. (48), we derive the complexity order for exhaustive algorithm as follows.
Exhaustive algorithm is formalized in Eq. (14) where the objective function is given as Eq. (3).
Therefore, the complexity of exhaustive algorithm S eh is given as follows.
In the proposed algorithm, we simplify the exhaustive algorithm into sequential selection of small cells through two-step transformations.
Although an exhaustive algorithm can achieve global optimal performance, its complexity is significantly high, which is impractical.
A most simple clustering algorithm is an exhaustive search of all possible clustering patterns (exhaustive algorithm) [20].
Similar(38)
Table 4 The comparisons of calculation times between different algorithms Optimization algorithms Optimization (step/MW) Calculation times The exhaustive algorithms 50 9216 The bilevel algorithms 50 326.
Exhaustive algorithms, for example, MDR [ 5], appear promising for small scale data sets.
Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms.
Often exhaustive algorithms may not be available, or when available they may be too slow in practice.
A cophylogenetic scenario is produced, but the computational cost is very heavy (and the number of optimal scenarios can be very high), especially when exhaustive algorithms are used.
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