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The Delaunay hierarchy has O(nlogn) time complexity and O(n) memory complexity in the plane, and under certain realistic hypotheses these complexities generalize to any finite dimension.
Regarding the memory complexity, in case of real-time processing it is of advantage to precompute and store the signal { e - j 2 π k ^ n N }.
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Although it is desirable to maximize w in order to be time efficient, the memory complexity given in Equation (1) suggests that we should minimize w (and c) in order to be memory efficient.
We take advantage, in time and memory complexity, of the data's sparsity, to allow clustering of large sets.
In our setting, | E| ∈ (|chd u)|), hence the total memory complexity of our dynamic programming algorithm is (n).
Both of the algorithms are characterized in terms of the trade-off between estimation performance, communication, computation and memory complexity.
(i) Memory complexity: memory needed to store keys.
The memory complexity of this method is.
Let us now analyze the memory complexity.
More implementation details, as well as worst case and expected time and memory complexities, can be found in Supplementary Section S2.
The article shows that this is reached at the cost of only a negligible rise in computational and memory complexities.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com