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The major computational cost of PDA-based algorithms is the calculation of Σ k −1 and the matrix multiplication of left(begin{array}{c} R(mathbf{w}) I(mathbf{w}) end{array}right)^{top} {mathbf{Sigma}_{k}}^{-1} left(begin{array}{c} R(mathbf{w}) I(mathbf{w}) end{array}right).
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The computing of synaptic currents occupies a major part of computational cost when simulating a large scale spiking neural network.
However, one of its major drawbacks is computational cost.
The traditional global search method, which searches the optimal position by scanning through all possible focus positions with constant step size, has two major limitations: high computational cost and suitability with only narrow focused image.
The major limitation is its computational cost because it requires to solve the sub scale at each gauss point and load step.
The integration of ODE often constitutes a major part of the computational cost in the parameter estimation, especially when the ODE model is stiff [ 7].
For multi-objective design of multi-parameter antenna structures, optimization efficiency and computational cost are two major concerns.
Computational cost is a major issue in modern large-scale simulations used across different disciplines of science and engineering.
Premature convergence and unacceptable computational cost are the major difficulties when conventional optimization algorithms are utilized in SpoT-based FIR filter design.
Thus, finding suitable features and identifying classifiers with low computational cost are the major areas of research activity.
But Bayesian structure learning algorithms had three major shortcomings, that is, the high computational cost, inefficiency in exploring qualitative knowledge, and the inability to reconstruct phenotype specific gene network.
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CEO of Professional Science Editing for Scientists @ prosciediting.com