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We use TVD Runge Kutta time discretizations to update the solution.
Let us update the solution of Equation 19 in the most relevant equations.
For a wavelet representation of the solutionuthat containsNssignificant coefficients, the algorithms update the solution usingO(Ns) operations.
The CUDA Fortran programming model is employed to develop the key kernel functions to apply boundary conditions, calculate time steps, evaluate residuals as well as advance and update the solution in the temporal space.
After obtaining the D i (1≤ i≤ c1+ c2) and D, we can update the solution V = [ W P] based on Equation (9).
Step 2: Equation B3 of Appendix 1 is used to calculate g ^ i = E [ g i | Y i ], which is used to update the solution for marker i.
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(4) where r 3 is a random number sampled in the interval [0,1] and sig(v j ) is defined as: text{sig}(v_{j}) = frac{1}{1+e^{-v_{j}}} (5) (c) Use the mutation operator as proposed by [45]; (d) Evaluate each particle of the swarm and update the solutions stored in the EA; (e) Update the particle's memory (hat {textbf {t}}); 3.
Use the mutation operator as proposed by [45]; Evaluate each particle of the swarm and update the solutions stored in the EA; Update the particle's memory (hat {textbf {t}}); END WHILE and return the current EA as the Pareto frontier.
The function updates the solution for all nodes and ghost nodes in the subdomain sd.
In 2000, Noor [9] first introduced a three-step iterative sequence and studied the approximate solutions of variational inclusion in Hilbert spaces by using the techniques of updating the solution and the auxiliary principle.
To overcome these drawbacks, Noor [2, 3] used the technique of updating the solution to suggest the two-step (or predictor-corrector) method for solving the variational inequality (1.2).
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