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The direct method becomes slow as the matrix size increases; therefore, it is not feasible practically.
○ The high-frequency minimization method reduces matrix size, increases compression ratio, and encrypts the matrix by using two different keys.
As the matrix size increases, the direct method becomes very slow, therefore not feasible practically, while the proposed method only calculates the desired eigenvectors via an iterative approach.
We believe that finding such designs in more structured way is still a challenging problem especially as the matrix size increases.
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In the case of the Binary tree, the number of branches is doubled as the matrix size n increases, being the increasing number of nodes equal to 2 n +1 - 1 [15].
This preconditioner, however, directly inverts a large matrix whose size increases in proportion to the number of time instances.
The recovery of the activity concentration in the spheres of a phantom is better when the matrix size is increased from 128∙128 (voxel size ∼5∙5 mm) to 256∙256 (voxel size voxels ∼2.5∙2.5 mm).
The possibility of extracting information based on less data is part of the reason for this technique's success, as it allows data compression/decompression, with an execution time that does not increase exponentially with increasing matrix size, making analysis viable [ 6].
Our results reveal that the four methods generally exhibit good performance scaling with increasing matrix size and number of cores.
For readability, we place the numerical results for these small sensing matrices in Appendix B. For each matrix size and type, we increased k from 1 to 5 in unit steps.
This finding was surprising, given that our data clearly show that new shapes arise as new matrix outgrowths, thus size increase certainly accompanies allometric reshaping.
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