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The idea of regularization is to enforce
The role of the two penalties is different, the l1 term (sum of absolute values) enforces the solution to be sparse, the l2 term (sum of the squares) preserves correlation among the variables.
The role of the two penalties is different, the l1 term (sum of absolute values) enforces the solution to be sparse, the l2 term (sum of the squares) preserves correlation among the genes.
{leftVert mathbf{x}rightVert}_1le c (1 where ℒ : ℝ D → ℝ is a convex and differentiable loss function, || ⋅ ||1 indicates an L 1 norm operator enforcing the sparse solution, and c is a constant for controlling regularization and sparsity, meaning how many zeros are in the optimal solution vector.
Moreover, it enforces the numerical solution to satisfy the exact interface conditions.
The forcing term is given as f x,t)=frac{Gamma(1+mu)}{Gamma(mu+1-alpha)}t^{mu-alpha }x^{3} 1-x -6kappa t^{3} 1-x -6kappaenforce the exact solution (u(x,t)=t^{3} 1-x -6kappa. toking (kappa =1), (alpha=0.9), (mu=2+alpha), Figurenforcecribes the bexactor of the global errorsolution1) versu xthe variation of p with (M=20) and (N=5mbox000).
We're enforcing the law".
enforcing the condition [14].
enforcing the normalization condition.
It employs a discretization scheme based on functional approximations and a collocation technique to enforce the global flow solution.
In particular, in place of L2 norm: L 2 ( f ) = f 2 2 = ∑ j f j 2, it has been proposed to use the L0 norm L 0 ( f ) = f 0 = ∑ j δ ( f j ) or the L1 norm L1(f) = ||f||1 = Σ j |f j | to enforce the sparsity of the solution [3 11].
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