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By the solution representation formula (2.3), we get (4.27).
We present below three different approaches in which this decision is directly determined by the solution representation.
By the solution representation, we construct the following extragradient thresholding algorithm (ETA) to solve the (ell_{1}) regularized projection minimization problem (5).
By the solution representation, we construct the following shrinkage-thresholding projection (STP) algorithm to solve the l 1 regularization projection minimization problem (6).
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Since the matrices L _ C _ M L, C, t, N p l u s M, t, N m i n u s M, t, V P, t and O P, t have been added to the solution representation designed by Kia et al. (2013), the procedure of generating an initial solution and neighborhood generation strategy need to be modified.
It follows from the solution representation formula (2.3) that (4.5).
Thus, we will only prove the solution representation formula (2.16).
The solution representation and the cardinality of the search space are also discussed in some cases.
In Figure 3, we demonstrated the solution representation for the proposed algorithm.
The GA is enhanced by a new solution representation and well selected operators.
We enhance genetic algorithm (GA) by using a new solution representation, examining and selecting best possible operators, and developing a local search mechanism.
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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