Specifically, in our study, we need the algorithm with ensured convergence property and fast enough convergence speed.
Note that, if needed, the algorithm can be easily parallelized as the p feature selection problems, as well as the different trees in an ensemble, are independent of each other.
Although more memory is needed, the algorithm exhibits better performance due to memory cache locality.
Third, we needed the algorithm to run on a single computer using commodity hardware, both to satisfy IRB concerns over data-duplication and reuse hardware already in place.
However, the algorithm needs the complete information of infection and recovery probabilities.
Therefore the algorithm needs the definition of the cooling schedule, the local search and the starting and stopping conditions.
If less accuracy is needed the stochastic algorithm is faster on short simulation time intervals.
Of course, you need the right algorithms to do it well.
The global convergence of Algorithm 1 needs the following assumptions.
Acceleration measurements of the sprung mass are used with a Kalman filter to estimate the displacements needed for the algorithm.
