MaxPairs algorithm is based on the approximation algorithm EPSILON-OPTIMAL [ 1], which is capable of finding a superposition of the input proteins that fits at least as many pairs of residues under the distance d + ε as an optimal superposition fits under the distance d, for any accuracy threshold ε > 0. As an approximation algorithm, EPSILON-OPTIMAL suffers from high computational complexity.
Since the number of system states grows exponentially with the number of channels, the realization of the optimal scheme suffers from the curse of dimensionality and is computationally overwhelming.
The maximum likelihood detectors (MLD) [3 5] which are the performance-optimal detectors suffer from exponential complexity in terms of the number of the transmitted signal, and they are not practical when the number of the transmitted signal becomes large.
The rationale is that if, even under these optimal circumstances, performance suffers, we can conclude that switching attentional sets comes at a cost.
The optimal controller can suffer from undesirable high-frequency noise characteristics and tuning methods for how to filter the control signal are also provided.
Compared to traditional methods such as the comb filtering method [23] and maximum likelihood methods [12, 16], the optimal filtering methods suffer from a high complexity, requiring that operations of cubic complexity be performed for each candidate fundamental frequency and order.
Outside this optimal range, plants suffer stresses which limit their growth and productivity.
Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets.
Hence, it is challenging to select models of optimal model complexity, which suffer neither from underfitting nor from overfitting.
However, conventional linear precoding schemes in massive MIMO systems, such as regularized zero-forcing (RZF) precoding, have near-optimal performance but suffer from high computational complexity due to the required matrix inversion of large size.
The very basic PSO model does not ensure convergence to an optimal solution and it also suffers from its dependency on external parameters like acceleration parameters and inertia weight.
