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On the other hand, in gradient-based methods, the gradient of objective function subject to optimization variables is needed.
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While in the gradient-based method, the gradient and fewer simulation runs are needed.
To overcome a possibly slow convergence of the gradient-projection method, the gradient is often scaled.
In our method, the gradient descent was stopped for a step of 10 HZ.
In this method, the gradient smoothing technique is used to facilitate formulation of boundary intersecting elements.
Figures 5(d)–5(g) are the fused results obtained by fusing Figure 5(b) and Figure 5(c) with the pixel averaging method, the gradient pyramid method, the DWT method, and the proposed method, respectively.
In this method the gradient field as well as the excitation field are switched rapidly and their amplitudes are calculated based on a Fourier series expansion.
Using the adjoint method, the gradient of the cost function can be computed fast, at the expense of few function evaluations, making the optimization process very efficient.
Using the approximate likelihood calculation method, the gradient g and Hessian H with BASEML using the GTR + Γ5 substitution model were calculated [ 82].
There are some parameter learning algorithms for complete data, including the maximum likelihood estimation method (Darwiche 2009), the Bayesian method, and the gradient descent method (Niculescu et al. 2006).
The LM method switches between the Gauss-Newton method and the gradient descent method.
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