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(1.4) where α plays a role of regularization parameter, data (g^{delta}) represents the measured data of function g.
The role of regularization is to impose desired characteristics on the image estimates to avoid this noise amplification and to increase the resolution.
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The penalty function is used to regularize the inversion and the smoothness parameter (tau) plays the role of a regularization parameter.
The role of the regularization term in (12) is illustrated next through a simple example.
The investigation also points to the limitations of the Oldroyd B model, with a potential finite time blow-up, and to the role of numerical regularization.
This includes a study of the influence of measurement noise on the reconstruction quality and tests of the role of the regularization parameter.
Moreover the role of the regularization parameter ε has been investigated; For practical purposes one can choose ε (almost) arbitrarily small and solve the discrete problem with a direct solver or by using the preconditioned conjugate gradient method.
Section 5 is devoted to the choice of the local length scale appearing in the Interior Penalty formulation and presents numerical experiments underlining the results of Sections 3, 4. Section 6 discusses the role of the regularization parameter ε and how to choose it.
Therefore the role of the regularization parameter α is to properly balance the information of the data and the prior knowledge.
To gain insight into the properties of (4) and the role of the regularization parameter λ2 we once again considering the case where X is taken to be orthonormal.
In the case of ill-conditioning and overfitting, we consider the use of regularization techniques.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com