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Fig. 2 Convergence tests.

Figure 2 Convergence of APD algorithm.

Figure 2 Convergence history for Sys.

Fig. 2 Convergence behavior of the proposed algorithm.

Fig. 2 Convergence of pressure signals using the Gradient Descent method.

Figure 2 Convergence behaviour of the iterative approach for different initializations with the direct link.

Otherwise, update and return to Step 2. Convergence Analysis.

Therefore, we shall use (11) to measure the (mathcal{O}(1/t^{2})) convergence rate of our new method.

Now let us review two different criteria to measure the worst-case (mathcal{O}(1/t^{2})) convergence rate in [28, 29].

Under mild assumptions, we establish the worst-case (mathcal{O}(1/t^{2})) convergence rate of PALM-IPR in a non-ergodic sense.

Under mild conditions, we have established the worst-case (mathcal{O}(1/t^{2})) convergence rate in a non-ergodic sense of PALM-IPR.