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The minimization in this paper is carried out by minimizing the cross entropy using conjugate gradients [22].
This solving strategy is called the ℓ1-norm minimization in this paper.
Since (gamma ) enters the potential (7) only through the (cos 3gamma ) dependence in the cubic term, the minimization in this variable can be performed separately.
The results demonstrate that lift-to-drag maximization is more appropriate than lift-constraint drag minimization in this case and that local search using multi-point space mapping can yield satisfactory design at low computational cost.
GPSM was proposed in [16] for derivative-free unconstrained optimization (minimization in this case) of continuously differentiable convex functions J : R n → R. The GPSM consists of a sequence of iterations H ˆ k nom, k ∈ N. At each iteration, a number of trial steps Δ h k n are added to the iteration H ˆ k nom to obtain a number of trial points H ˆ k n = H ˆ k nom + Δ h k n at iteration k.
The energy minimization in this algorithm is achieved by obtaining the derivative of the force equations.
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In this paper, we present numerical evidence that supports the notion of minimization in the sequence space of proteins for a target conformation.
In this paper, we focus on the estimator based on the ℓp-norm minimization in the presence of zero-mean symmetric non-Gaussian noise.
This paper presents a case study of pollution prevention and waste minimization in the North American gold mining industry.
This shows that the weighted ℓ2,1 minimization outperforms the regular ℓ2,1 minimization in the sense that a few snapshots are employed to obtain exact sparse recovery.
The analysis evinced maximization and minimization in the excitation rate at some particular dopant coordinate.
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