Differentiability of Distributions is Continuous with Respect to Weak Convergence
📂Distribution TheoryDifferentiability of Distributions is Continuous with Respect to Weak Convergence
Theorem
The differentiation of a distribution is continuous with respect to weak convergence. In other words, if Tk weakly converges to T, then ∂αTk weakly converges to ∂αT.
Tk→Tweakly⟹∂αTk→∂αTweakly
Here, α is any multi-index.
Explanation
It means that the differential operator of a distribution satisfies the condition for being continuous with respect to weak convergence.
n→∞limf(pn)=f(p),∀{pn}s.t.n→∞limpn=p
The significance of this fact for distributions is that it does not hold for pointwise convergence or uniform convergence.
fk→fpointwise or uniformly⟹fk′→f′pointwise or uniformly
That is, for a sequence of functions fk that converges to f, it is not guaranteed that fk′ converges to f′. However, in the case of weak convergence, this is guaranteed.
Proof
Let’s assume that the sequence of distributions Tk weakly converges to T. By definition of the differentiation of distributions, for any test function ϕ, the following holds:
(∂αTk)(ϕ)=(−1)∣α∣Tk(∂αϕ)
Taking the limit k→∞lim on both sides, by assumption, we have:
k→∞lim(∂αTk)(ϕ)=k→∞lim(−1)∣α∣Tk(∂αϕ)=(−1)∣α∣T(∂αϕ)=(∂αT)(ϕ)
Therefore, the following holds:
∂αTk→∂αTweakly
Thus, if Tk weakly converges to T, then ∂αTk weakly converges to ∂αT.
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