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Moreover when diagonal isolation cannot be obtained, an almost isolation method, based on a functional minimization, is proposed.
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The problem for designing PALs could be viewed as a functional minimization problem.
The minimization of the error on a functional provides both the density and the anisotropy (stretching) of the optimal mesh.
Numerical results for linear and nonlinear problems demonstrate the good performance of the cell functional minimization scheme and its parallel version on distorted meshes.
The representor theorem greatly simplifies the optimization task by turning the minimization over a functional space into the minimization over a finite-dimensional vector space.
The algorithm is based on the minimization of a functional depending on a least square error term combined with two penalties.
The algorithm is based on the minimization of a functional depending on a least square error term combined with a regularization term, i.e., the l2 term (sum of the squares).
It based on a direct minimization of an energy functional containing a minimal surface regularizer that uses fractional gradient.
It is based on a direct minimization of the appropriate energy functional containing a "minimal surface" regularizer with the fractional gradient.
Gradient minimization methods need the gradient of a functional to be minimized.
The existence arguments in both works rely on a minimization of the penalized energy functional on the nodal Nehari set, and the concentration arguments follow the same general lines.
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