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The fast convergence rate of the methods is demonstrated and their accuracy is verified by comparing the results with those obtained using ANSYS and also with available exact solution of a particular problem.
Therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested.
The simulation results can be alternatively represented by focusing on the sensitivity (i.e. the true positive rate) and specificity (i.e. the complementary of the false positive rate) of the methods.
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The typical convergence rate of the method is (O ( frac{1}{m} )) as shown in the numerical results.
Under mild assumptions, we prove the global convergence and linear convergence rate of the method.
Finally, the convergence rate of the method is compared with MGM and BSA methods.
The overall convergence rate of the method is of order q.
We used a second-order algorithm for solving the Langevin equation, and we studied the convergence rate of the method.
We present numerical experiments for q = 4, 8, and 16, and we verify the theoretical convergence rate of the method.
We carry out several numerical test cases to examine the CPU time and convergence rate of the method.
Experimentally, it was found that this square factor was important to improve the success rate of the method (more details can be found at [4, p. 148]).
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