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Moreover, the eigenmodes (and accordingly the set S c of scheduled UEs and the transmission rank allocated to each UE k ∈ S c ) are selected by using a greedy iterative algorithm which, at each iteration, includes the eigenmode which maximizes the weighted sum rate R ̂ ( c ) among the ones not scheduled in the previous iterations.
Then comprehensive sets of correlated seed biclusters are expanded to larger biclusters using a greedy iterative heuristic approach with the Pearson correlation coefficient as the scoring function.
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To avoid computational issues in biclustering, most existing biclustering algorithms use a greedy iterative heuristic approach that locally improves an appropriate scoring function starting from initial seed biclusters.
Some biclustering methods use a greedy iterative search strategy to uncover biclusters, progressively subdividing, or adding and removing rows and columns from the biclusters obtained in a previous iteration in order to maximize a local score function [ 12- 15].
(3) Minimize the global normalized cut using a greedy strategy.
In this work a starting solution for this problem is found by using a greedy algorithm.
The space of models is searched using a greedy algorithm.
The solution can also be parallelized (fourth technique) using a greedy algorithm.
The hyperparameters of these multiple sparse priors are optimized using a greedy search.
These local skeletons are then directed using a greedy hill-climbing algorithm.
They were matched 1-to-3 according to age and gender using a greedy algorithm.
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