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An optimization method that matches hourly supply and demand may encounter problems of exhaustive computing time and 'out of memory' because size of associated matrices increases in multiples of 8760 × 8760.
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As PharmGKB is focused specifically on curation of gene drug relationships, it is also possible that evaluation against this resource is not representative of the general problem of exhaustive annotation of genetic variants.
In this paper, we consider the problem of exhaustive counting and discovery of pairs of subwords that co-occur more frequently than expected within a specified distance in a sequence.
To overcome the complexity problems of the exhaustive search for mappings of higher order constellations, the binary switching algorithm (BSA) [9] is implemented to find the optimal mapping, by means of searching for the best cost function (CF) based on the characteristics of the distance spectrum.
The usual method to obtain the optimal result of such types of problem is exhaustive searching which is extremely difficult, i.e., NP-hard [29,30].
The above optimization problem can be solved by means of exhaustive search or meta-heuristic algorithms [9, 13].
Some algorithms such as Radon and Hough transforms [13] and polar mapping [15] can solve this problem by an iterative optimization implementation of exhaustive searching in a wide angle range.
Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms.
For this problem, an exhaustive search of all possible subsets of relays is needed to attain the optimum relay combination.
With properly modeling the problem and applying machine learning algorithm, our method can accurately predict the optimal decision on CU splitting instead of exhaustive searching over all possibilities.
For this relay selection problem, the exhaustive search of all possible relay pairs is the optimum way to obtain the best performance.
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