Often, module-discovery is performed by attempting to maximize a modularity measure, but in principle neither does a modularity measure imply an algorithm for module discovery, nor does a module-discovery algorithm necessitate a measure of modularity.
This implies a iterative algorithm.
However, according to Wada and Perron (2006), the inclusion of all disturbances as mixtures of normals implies an unstable estimation algorithm.
This immediately implies an O kn) time algorithm for the TBR-S problem (see Observation 1).
This implies that an algorithm yielding constant estimates independent of any data could achieve WGE-Inter = 0, but it would obviously do very poorly on WGE-True.
However, typically this case does not exist as it would imply an iterative algorithm without initial condition.
The main conclusion we can extract from this time comparison is the fact that DTW algorithm implies an increment of the computational time mainly for high dimensional feature vectors like CC, SC and FD.
The fact that all sets are of size at most six also implies a 6-approximation algorithm for the line graph node deletion problem [ 15].
However, selecting the best tool a priori is risky, as tool popularity, journal impact, and citation frequency do not necessarily imply a particular algorithm is optimal3,61.
For a leasing variant of the buy-at-bulk network design problem, these results imply: (i) an algorithm which improves the best previous approximation, and (ii) the first competitive online algorithm.
