Distributional derivative of one-parameter family of distributions

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SUMMARY

The discussion focuses on the distributional derivative of a one-parameter family of distributions, denoted as {G_x}, where G_x: T(ℝⁿ) → ℝ for all x ∈ ℝⁿ. It establishes that a linear differential operator L can act on distributions, with the distributional derivative defined as ∂/∂x_i G_{x_0}(g) = G_{x_0}(-∂/∂x_i g). The relationship between L and the function ψ_g(x) = G_x(g) is explored, specifically questioning when the equality L(ψ_g)(x_0) = (L G_{x_0})(g) holds for all x_0 ∈ ℝⁿ.

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  • Understanding of distribution theory and test functions T(ℝⁿ)
  • Familiarity with linear differential operators and their properties
  • Knowledge of distributional derivatives and their definitions
  • Concept of smooth functions and their interaction with distributions
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  • Research the properties of linear differential operators in distribution theory
  • Study the concept of distributional derivatives in detail
  • Explore examples of one-parameter families of distributions
  • Investigate the conditions under which L(ψ_g)(x_0) = (L G_{x_0})(g) holds
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Mathematicians, theoretical physicists, and students studying functional analysis or distribution theory who seek to deepen their understanding of distributional derivatives and their applications.

Only a Mirage
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Suppose, for a suitable class of real-valued test functions T(\mathbb{R}^n), that \{G_x\} is a one-parameter family of distributions. That is, \forall x \in \mathbb{R}^n, G_x: T(\mathbb{R}^n) \to \mathbb{R}.

Now, suppose L is a linear differential operator. That is, \forall g \in T(\mathbb{R}^n) makes sense in terms of the normal definitions of derivates (assuming, of course, that g is sufficiently smooth). L also has meaning when acting on distributions by interpreting all derivatives as distributional derivatives. For example, the derivative of the distribution \frac{\partial}{\partial x_i} G_{x_0} is defined by: \forall g \in T(\mathbb{R}^n), \frac{\partial}{\partial x_i} G_{x_0}(g) = G_{x_0}(- \frac{\partial}{\partial x_i}g).

Note that smooth functions can multiply distributions to form a new distribution in the following way. Suppose f:\mathbb{R}^n \to \mathbb{R} is a smooth function. Then f G_x is defined by: \forall g \in T(\mathbb{R}^n), (f G_x) (g) = G_x(f g)

These facts give L G_x meaning.

Also, for fixed g \in T(\mathbb{R}^n), (x \mapsto G_x (g)) is a possibly (non-smooth?) function. Define the function \psi_g : \mathbb{R}^n \to \mathbb{R} by \psi_g (x) = G_x(g)

Now, here is my question: When is the following equality true?

L (\psi_g) (x_0) = (L G_{x_0}) (g), \forall x_0 \in \mathbb{R}^n
 
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