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However, these conventional methods cannot guarantee to find the global optimum because of the non-convex, non-differential and non-linear nature of the ORPD problem [6].
Because GA is a search heuristic that mimics the process of natural evolution, it cannot guarantee to find the best solution after only one run (Marian et al. 2008a, b).
In this case, although our algorithm cannot guarantee to find the GMEC solution, we can still know whether the solution returned by SMA* is a GMEC solution.
In fact, CCC-biclustering is also an exhaustive method [ 20], but it adopts a significant trend filtering to handle with the data pre-processing and thereby cannot guarantee to find all potential scaling patterns/linear patterns.
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When we restricted the memory to 1%, GSMA*768 can still keep all GMEC solutions, though it cannot theoretically guarantee to find the GMEC solution in some cases.
In short, you're paying for something they cannot guarantee to get.
Since the GA starts by randomly generating an initial population, it cannot guarantee to obtain an optimal solution.
The procedure also does not guarantee to find test signals for every given system, because it cannot automatically derive test signals from system specifications, meaning that the equations for signal synthesis have to be invented by the test designer.
As a known NP-hard MCF problem, it cannot be guaranteed to find an assignment to satisfy all the flows for all kinds of traffic matrix all the time.
As such, it cannot be guaranteed to find the optimum partitioning scheme for any given dataset.
FCmotif overcomes drawbacks of traditional algorithms which are time-consuming and cannot handle the full size data; it guarantees to find all potential (l, d) motif instances.
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