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(7) Iterate through steps (1)–(6) with r 1(i ) instead of s (i ) until the residue satisfies some stopping criterion as (2) SD = ∑ d i − s i 2 ∑ s 2 (i ) < α, where α is an arbitrary value in the range of 0.2 0.3 as recommended in [ 15 ].
Step 5. Iterate through Steps 3 and 4 until no significant changes occur, i.e. until convergence.
Iterate through steps (1)–(6) with r1(i) instead of s(i) until the residue satisfies some stopping criterion as (2) SD = ∑ d i − s i 2 ∑ s 2 (i ) < α, where α is an arbitrary value in the range of 0.2 0.3 as recommended in [ 15].
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The k-means clustering algorithm will iterate through the following steps: 1. Randomly initiate K random clusters 2.
Our approach iterated through four steps: data collection and preprocessing, hypothesis generation, model evaluation and experimental feedback (Figure 1).
In the second step, we iterate through the set of uncommon deletions U, and remove an uncommon deletion u ∈ U, if there exists a common deletion c ∈ C which is a substring of u.
For p=2, we iterate through the linked list A1 [ f1(2)].
Higher order approximation can be iterated through the previous steps.
It iterates through the combinations (step 1.1).
The algorithm (Algorithm 1) consists of iterating through the event point schedule and updating the regions of intersection found at each step according to whether the event point is an addition, removal, or intersection.
A Letters instance can only be iterated through once.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com