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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 typical convergence rate of the method is (O ( frac{1}{m} )) as shown in the numerical results.
The overall convergence rate of the method is of order q.
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.
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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.
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.
Numerical experiments on problems with known exact solutions are performed for verifying the expected convergence rates of the method.
For patients 17, 18, and 21, the false-positive rates of the method with dynamic model update are much lower than the method without update.
The progression-free survival rate of the new method calculated by using the Kaplan-Meier method was 60.2%.
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