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The presented theoretical results provide a guideline on how to design experiments that minimize the worst-case identification error, as measured by the radius of information of the set of feasible model parameters, calculated in any norm.
This proves that is bounded in maximum column sum matrix norm ([1, page 294]), and hence in any norm in.
If (Hvarphi =Evarphi ) for (varphi in L^2), then (e^{-itH}varphi =e^{-itE}varphi ) has no decay in any norm.
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∥ ⋅ ∥ n and ∥ ⋅ ∥ k denote any norm in R n and R m, respectively, 3.
This means its Frobenius norm is bounded as well and by the equivalence of norms, so is any norm, in particular (||left (boldsymbol {C_{G}^{T}}right)^{i}||_{tau }).
From the equivalence of any norm in finite-dimensional space, it follows that int_{0}^{T} {biglvert v_{n} (t -v t) bigrvert -v t,dt} to0quad mbigrvert ntoinfty.
h ∈ R n, ∥ ⋅ ∥ n and ∥ ⋅ ∥ k denote any norm in R n and R m, respectively, ∇ F ˜ i ( x ) denotes the gradient of F ˜ i at x for i = 1, 2, …, k.
This positive definite matrix R was then used to define the weighted norm ∥⋅∥R in Cn which led to new two-sided bounds on the solution of the initial value problem ẋ="Ax,x t0)="x0, in any vector norm ∥⋅∥.
Since all norms on are equivalent, the limit in (d) can be relative to any norm on, so that (d) is equivalent to all the entries of converge to zero as, which in turn is equivalent to for all.
for all x, y ∈ G with any norm ∥ ⋅ ∥ in K n.
for all x, y ∈ G with any norm ∥ ⋅ ∥ in K n (see [6] for related results).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com