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This method converges quickly.
Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem.
This method converges quickly, typically locating an appropriate C* in fewer than 10 iterations.
For the following iterations, if the least squares norm || ỹ – J̃ h k+1/2|| < || ỹ – J̃ h k|| then λ k+1 is decreased by a certain percentage of the current value, p λ, since the Gauss-Newton method converges quickly near a minimum.
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The optimization method converged quickly after around 300 iterations in the same optimal area.
Many test statistics, e.g. those derived by the maximum likelihood method, converge quickly to a normal distribution when the sample size increases.
For problems where the characteristics are straight lines, sweeping methods converge quickly and are often faster than tracking methods.
Examples show that this method converges very quickly and good results are obtained.
Our multiscale method converges very quickly (within 3 4 iterations) and is an order of magnitude more computationally efficient than the full-scale simulation.
It is an efficient iterative method which converges quickly.
We set up the GA to leap all the greedy traps of a local minimum in the path toward the optimum value and then we opt for the gradient-based Quasi-Newton method to converge quickly for the best parameters possible.
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CEO of Professional Science Editing for Scientists @ prosciediting.com