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By comparing these three algorithms, it is clear that the reweighted l 1 minimization algorithm performs better in terms of SNR than the chaotic iterative algorithm, and the A T Bregman iteration lemma is a little slower than the chaotic iterative algorithm and the A T Bregman iteration, which is still acceptable.
Lemma 3.1 (Iteration lemma).
Thus all the parameters for (H_) are defined, and so Iteration Lemma is proved.
Let where From the iteration lemma, we have that for all the equation (2.57).
To this end, we need a generalized iteration lemma, which can be found in [9, Proposition 2.1].
Finally, in the Appendix, we give a proof of the iteration lemma (Lemma 3.1) for the sake of readability.
Similar(50)
Since f 2 ′ ( 0 ) = − σ 2 2, we can obtain the upper bound for the decreasing value of the proximity in the inner iteration by Lemma 3.7.
The algorithm succeeds in finding approximate zero in a finite number of iterations due to Lemma 1, the theorem and its proof.
and reabsorbing at the right-side first integral in the inequality above by a covering and iteration argument (see [21, Lemma, Chapter 2], or [22, Lemma, Chapter 3]), we have (3.48).
This is due to the fact that despite the different iteration scheme at hand, Lemma 7.5 (though by a different proof) is identical to [[1], Lemma 9.2].
Now, we prove the following lemma using iteration scheme (2.6) needed in the sequel.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com