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It is not difficult to prove this result by using the maximum norm.
We will measure the accuracy of the proposed scheme using the maximum norm errors defined by e^{n}_{epsilon}=bigl| v^{n}-u^{n}bigr| _{infty}.
Different from traditional optimized schemes that use the 2-norm and the least squares, we propose to construct the objective functions using the maximum norm and solve the objective functions using the simulated annealing algorithm.
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In our estimates, we use the maximum norm given by ∥ v ∥ ∞ = max [ 0, T ] | v ( t ) |.
We have a critical estimation for the maximum norm which will be used for stability and convergence analysis.
Notice that "any hyperconvex norm on " means essentially (i.e., up to an isometric isomorphism) the maximum norm; this follows from Theorem 2.6 and can also be proved using a geometric argument (see [19, Theorem 4.1]).
Using a Lipschitz stable interpolation and a semi-Lagrangian scheme, our method is stable under both the maximum norm and the Lipschitz semi-norm.
where ∥·∥ ∞ is the maximum norm.
∥·∥2, (| cdot |_{infty }) and ∥·∥ F symbolize the Euclidean norm, the Maximum norm and the Frobenius norm, respectively.
An a posteriori error estimation in the maximum norm is derived.
Even in the maximum norm, the pressure gradient appears to converge to the initial pressure gradient.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com