To further optimize the greedy EDM FDE algorithm for reduced computation time, several strategies can be implemented. One approach is to parallelize the eigenvalue decomposition process, which can significantly speed up the computation by distributing the workload across multiple cores or processors. By leveraging parallel computing techniques, the algorithm can exploit the capabilities of modern multi-core systems to perform the eigenvalue decomposition step more efficiently.
Another optimization strategy is to utilize more efficient matrix decomposition methods that can reduce the computational complexity of the algorithm. For example, using specialized algorithms or libraries optimized for eigenvalue decomposition, such as the divide and conquer strategy implemented in LAPACK, can improve the speed of the computation. By selecting the most suitable matrix decomposition method and optimizing the implementation for performance, the greedy EDM FDE algorithm can achieve faster fault detection and exclusion while maintaining accuracy.