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On the other hand, N should be as small as possible to limit the computational complexity.
High-order approximations are then introduced to limit the computational cost associated with variability management.
However, we have considered memories of N=1 or N=0, not necessarily N=MTx, to limit the computational complexity.
It is possible to limit the computational of the cancellation algorithm by arresting it after stage S (S
Moreover a bounding technique is proposed to limit the computational time required to solve the lower-level problem.
To limit the computational load, we propose to reuse the same set of Haar-features for each of these steps.
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Hence, limiting the computational cost of the classifier is equivalent to limiting the number of features used for classification.
In the network filtering stage, the algorithm stores a buffered network of size N b, limiting the computational complexity and memory usage of the algorithm.
Accordingly the proposed approach is shown to mitigate convergence at a sub-optimum design while concurrently limiting the computational resources required during the optimization cycle.
Systems with shorter-range correlation and/or smaller concentration variance require more particles, potentially limiting the computational feasibility of the method.
For numerical solution, upwind explicit schemes which have been applied so far generally require extremely small time intervals (10−2 10−3 s) for numerical stability, thus limiting the computational efficiency.
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