Sentence examples for exhaustive solutions from inspiring English sources

flexible exhaustive solutions: arbitrary number of (potentially overlapping) biclusters with additive, multiplicative and symmetric assumptions using multiple ranges of values; biclustering behavior dynamically adapted to deal with varying levels of noise and missing values.

Results show BicPAM's superiority against its peers and its ability to retrieve unique types of biclusters of interest, to efficiently deliver exhaustive solutions and to successfully recover planted biclusters in datasets with varying levels of missing values and noise.

BicPAM approaches integrate existing disperse efforts towards pattern-based biclustering and provides the first critical strategies to efficiently discover exhaustive solutions of biclusters with shifting, scaling and symmetric assumptions with varying quality and underlying structures.

This is an exhaustive solution to a (physically) small problem.

In the following, the proposed solution is compared to the optimal exhaustive solution for a special case with low users and RB number for evaluation.

In addition, the use of molecular fragments, as often practiced in the de novo design of novel structures, can lead to a combinatorially large search space which becomes intractable for exhaustive solution techniques.

To get rid of this complexity, we point out that the above-mentioned ideal (exhaustive) solution can also be viewed as an exhaustive combination of intervals (J N combinations) associated with an exhaustive combination of elements within each interval combination (P N combinations).

And, we observe that the most important part of this exhaustive solution pertains to the combination of intervals, i.e., the combination of elements belonging to interval j of typical set λ n S n ℓ with elements belonging to interval k, k ≠ j typical set λ m S m ℓ, m ≠ n.

Our sensitivity analyses indicate that, under all the concurrent constraints considered, large deviations from the specified values of these critical parameters, and hence radically different solution characteristics, would not be expected, if an "exhaustive" solution search to the particular adaptation problem was attempted.

The problem of training these models is an optimization task that can be solved with stochastic search methods (Saez-Rodriguez et al., 2009), which have the important limitation that they do not guarantee global optimality nor an exhaustive solution.

We have shown using several numerical examples the efficiency of the obtained equilibrium compared to the exhaustive (optimal) solution and a fixed full frequency reuse pattern.