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Figure 9 is the same as Fig. 4 but using a fixed step size and uniform noise.
First, the n-dimensional design space is globally explored following those Peano curves, which are obtained by varying only 1 design variable at a time using a fixed step size.
To obtain the bitrates of the coder, we first run the predictor using a fixed step size Δ on a large data set of mixed audio having a sampling frequency of 48 kHz.
In the subsequent algorithm iterations (over time), the employed threshold value is updated (incremented or decremented) using a fixed step size, RSRP_Step or RS S_Step based on the outcome of the cell load check sequence.
Second, unlike the Sl0 using a fixed step size to control the decrease of the parameter σ, which determines the degree of smoothness and the approximation accuracy of l0-norm, l2-Sl0 uses a variable one.
Moreover, unlike Sl0 using a fixed step size to control the decrease of the parameter σ, l2-Sl0 uses a variable one, and the step size δ r slightly increasing with the reduction of σ may also help the l2-Sl0 to improve its estimation accuracy.
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Note that these iterative algorithms also used a fixed step size.
Both the CQ algorithm and the relaxed CQ algorithm used a fixed step size and need to know the largest eigenvalues of the operator (A^A).
The CQ algorithm used a fixed step size, which relies on the spectral norm of the linear operator A. Qu and Xiu [6] improved it by using the Armijo-like search method to solve the SFP, for which there is no need to know the prior norm of A. They also used the Armijo-like search way to a relaxed CQ algorithm.
Both the CQ algorithm and the relaxed CQ algorithm used a fixed step size related to the largest eigenvalue of the matrix (A^ A) or the spectral radius of the self-adjoint operator (A^A), which sometimes affects convergence of the algorithms.
Because we found that implicit methods implemented in MATLAB failed here, we used a fixed step size that was manually adapted to the fast timescale, making the simulation calculations rather slow.
More suggestions(15)
using a fixed linear
using a fixed window
using a fixed amount
using a fixed threshold
using a fixed t
using a fixed interaction
using a fixed stadiometer
using a single step
using a fixed skip
using a fixed grid
using a fixed learning
using a multiple step
using a fixed size
using a fixed nonlinear
using a fixed mesh
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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