Additionally the computational speed of the algorithm is compared with the general convex optimizer cvx.
Furthermore, a sub-optimal initialization strategy is proposed, which allows to improve the convergence speed of the algorithm.
The speed of the algorithm is evaluated by means of the number of objective function evaluations and time of the optimisation.
The novel TP algorithm is robust and a flexible method is developed to balance the optimality and speed of the algorithm.
The robustness and speed of the algorithm is demonstrated through a series of examples involving thin bridges, coatings, narrow necks, corners, cusps, and random mixtures.
It is shown that the convergence speed of the algorithm depends not only on the spectral properties of the input signals but also on the multiple secondary paths.
This level of accuracy along with the fast speed of the algorithm is highly desirable for the estimation of the TOC parameter.
Furthermore, a point tracking method is developed to improve the robustness and speed of the algorithm by predicting the position of non-coded target.
Furthermore, to accelerate the speed of the algorithm, we made use of the recursive property of the bounding function together with a lookup table.
In addition, the inversion-free nature and consequent speed of the algorithm avoid excessive computational time, thus making it suitable for large distribution networks.
This paper suggests using a method of evaluation which changes as the algorithm evolves: whereas accuracy is initially compromised to improve the speed of the algorithm, the process is subsequently altered to produce a more accurate, evaluation process.
