In this work, the protein was kept rigid during ligand-protein complex minimization to reduce the computational expense.
Finally, we emphasize that the superior performance of our weighted-l2-norm solution in retrieving sparse residuals in Section 5.2, plus the slight improvement of the coding quality in Section 5.3, was achieved with roughly the same computational complexity as the classical l2-norm minimization (if we neglect the computational burden of the GCI detector).
Then we will discuss the effects, in terms of the resolution in the time-frequency domain and the minimization of the required computational complexity, of window width, order mismatch, i.e., using the second order LPFT to process higher-order PPS, and the length of overlap between signal segments.
The algorithm was also optimized for minimizations with GB solvent that increases the computational expense by only a factor of ∼3 relative to the vacuum.
The computational cost for their minimization will limit the real information capacity of such a model.
The computational details, including the minimization and equilibration procedures and initial position of the ligand, were the same as for the wild-type receptor.
Similar to the recently proposed mean-first-passage-time (MFPT) control policy, these policies do not depend on minimization of a cost function and avoid the computational burden of dynamic programming.
To reduce the computational burden, a fast algorithm to compute the minimization of J is developed in Appendix, where the closed-form solution to the estimate of φ i is directly obtained by using the fast algorithm.
The computational results demonstrate that simultaneous minimization of energy and room temperature ramp rate is more beneficial than minimization of energy only.
Since that reweighted minimization can enhance the sparsity and the chaotic iterative algorithm can reduce the computational complexity of the generalized inverse A †, we iteratively solve the following weighted ℓ 1 minimization problem: min u { ∥ u ∥ ω : A u = f }. (4.3).
