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The proposed approach employs a sparsification method based on an independence measure.
Furthermore, including additional depot arcs into the restricted arc set (beyond those selected by the sparsification method) improves solution quality.
Moreover, in order to alleviate the high complexity caused by GPR, a sparsification method is also put forward.
In order to verify the improvement provided by the proposed sparsification method in each of the three steps of the source separation, we proposed some simulation scenarios.
In other terms, since this step was a critical issue in the sparsification method of [26], what is the effect of the synthesis through the overlap-add method?
This paper presents another sparsification method for the APSM approach to the online classification task by generating a sequence of linear subspaces in a reproducing kernel Hilbert space (RKHS).
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So-called sparsification methods are applied to restrict the neighborhoods to include only elements which are likely to be part of high-quality solutions.
In extensive numerical experiments with a granular tabu search (GTS) for the vehicle-routing problem with time windows (VRPTW), we find that sparsification methods using reduced-cost values based on the solution of a linear relaxation of the original problem outperform standard sparsification methods.
This property is very useful in nonstationary conditions while other existing sparsification methods keep increasing the network size.
Third, and critically, we use statistical sparsification methods (specifically, L1-regularization) to cope with the fact that our statistical model is based on a large number of redundant, noisy features.
Sparsification is a method that improves significantly the computation of minimum free energy (mfe) RNA structures.
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