Question

Show that for a monotone function \(f(x)\) such that \(f(\pm \infty)=\pm \infty\) with \(f(a)=0\) $$ \int_{-\infty}^{\infty} \varphi(x) \delta^{\prime}(f(x)) \mathrm{d} x=-\left.\frac{1}{f^{\prime}(x)} \frac{\mathrm{d}}{\mathrm{d} x}\left(\frac{\varphi(x)}{\left|f^{\prime}(x)\right|}\right)\right|_{x=6} $$ For a general function \(f(x)\) that is monotone on a neighbourhood of all its zeros, find a general formula for the distribution \(\delta^{\prime} \circ f .\)

Short answer

The steps derived the formula \(-\left.\frac{1}{f^{\prime}(x)} \frac{\mathrm{d}}{\mathrm{d} x}\left(\frac{\varphi(x)}{\left|f^{\prime}(x)\right|}\right)\right|_{x=a}\) for the given function and generalized it to \(-\frac{1}{f^{\prime}(x)} \frac{\mathrm{d}}{\mathrm{d} x}\left(\frac{1}{\left|f^{\prime}(x)\right|}\right)\) for \(\delta^{\prime} \circ f\) for a general monotone function.

Step-by-step solution

Express the integral using the properties of δ’(f(x))

By the property of the derivative of the delta function, we can write the integral as \[ \int_{-\infty}^{\infty} \varphi(x) \delta^{\prime}(f(x)) \mathrm{d} x = - \int_{-\infty}^{\infty} \varphi^{\prime}(x) \delta(f(x)) \mathrm{d} x. \] Since \( \delta^{\prime}(f(x)) \) is a distribution, it is better to represent the integral in this form which allows more comfortable manipulation than the original.

Apply the properties of the delta function δ(f(x))

Now we express the integral in terms of the values at the zeros of \(f(x)\). It is known that a delta function picks a value at the zeros of the function. Thus, the integral can be written as \[ \int_{-\infty}^{\infty} \varphi^{\prime}(x) \delta(f(x)) dx = \varphi^{\prime}(a), \] where \( a \) is the point at which the function \( f(x) \) is zero, as is specified in the problem.

Express \(\varphi^{\prime}(a)\) in terms of \(f(x)\)

Notice that by chain rule of derivative, \(\varphi^{\prime}(a) = \left(\frac{\mathrm{d}}{\mathrm{d} x}\left(\frac{\varphi(x)}{\left|f^{\prime}(x)\right|}\right)\right)_{x=a} \cdot f^{\prime}(a)\). Since \(f(a) = 0\), and \(f^{\prime}(a)\) is nonzero because \(f(x)\) is monotone, we can easily compute it as \[\varphi^{\prime}(a) = -\left.\frac{1}{f^{\prime}(x)} \frac{\mathrm{d}}{\mathrm{d} x}\left(\frac{\varphi(x)}{\left|f^{\prime}(x)\right|}\right)\right|_{x=a},\] giving us the desired proof for the first part of the exercise.

Finding a general formula for \(\delta^{\prime} \circ f\)

This part of the exercise is a generalisation of the preceding steps. Using the results derived from the previous steps, we can come up with a generalized solution: \[ \delta^{\prime} \circ f = -\frac{1}{f^{\prime}(x)} \frac{\mathrm{d}}{\mathrm{d} x}\left(\frac{1}{\left|f^{\prime}(x)\right|}\right)\] This formula characterizes the distribution function \(\delta^{\prime} \circ f\) for a general monotone function.

Key concepts

Monotone Function

A monotone function is a type of function which consistently moves in one direction. Specifically, it is either non-decreasing or non-increasing for all values in its domain. This quality makes monotone functions particularly easy to understand and work with, especially when dealing with limits or derivatives.

Monotone functions have the following key properties:

• If a function is increasing (non-decreasing), then whenever you pick two values, say \( x_1 \) and \( x_2 \), if \( x_1 < x_2 \), then \( f(x_1) \leq f(x_2) \).
• Conversely, if the function is decreasing (non-increasing), then for the same \( x_1 \) and \( x_2 \), \( f(x_1) \geq f(x_2) \).

This behavior ensures that the function does not oscillate and generally approaches specific values as \( x \) tends towards infinity, making it straightforward to predict.

Whenever you work with monotone functions and the delta function in distribution theory, it becomes crucial to understand these properties since they directly affect how solutions are approached.

Distribution Theory

Distribution Theory offers a powerful framework for analyzing functions, particularly generalized functions like the delta function. A function like \( \delta(x) \) is not a function in the traditional sense but rather a distribution. It is defined by how it interacts with test functions, allowing the evaluation of more complex mathematical scenarios. This theory extends classical analysis to accommodate objects like distributions which are not defined at specific points.

In this context:

• The Dirac delta function \( \delta(x) \) is often used to model point charges or impulses that are infinitely concentrated. It has the peculiar property that it is negligible everywhere except at a single point, where it 'captures' all value.
• Distributions, including \( \delta(x) \), are integral to working with differential equations and models that describe real-world phenomena, such as signal processing and quantum mechanics.

This theory leverages integrals over these distributions to simplify expressions and manage problems that would be intractable otherwise. Understanding how distributions work, especially alongside monotone functions, is essential for effectively using this powerful mathematical toolset.

Derivative of Delta Function

The derivative of the delta function, often represented as \( \delta'(x) \), is an even more intriguing concept within distribution theory. It is applied primarily in expressing how the rate of change or the slope adjusts around a pinpoint characteristic like the delta function.

To better understand the concept:

• The derivative of \( \delta(x) \) does not exist in the classical sense since \( \delta(x) \) itself is not a regular function. Instead, \( \delta'(x) \) operates within the realm of distributions.
• As a result, it is primarily analyzed through integration. For instance, when integrated against a test function \( \varphi(x) \), it behaves as \( -\varphi'(0) \), reflecting how much \( \varphi(x) \) changes at \( x=0 \).

The manipulation of such derivatives, particularly with monotone functions, requires precise understanding and careful consideration to accurately capture behavior at those key points.

Chain Rule

The chain rule is a fundamental principle when considering composed functions in calculus. It allows for the differentiation of nested functions by connecting their derivatives in a seamless linkage.

Here's what you need to understand:

• For a composite function \( g(f(x)) \), the derivative can be calculated by the formula \( (g(f(x)))' = g'(f(x)) \cdot f'(x) \). This rule is crucial when breaking down complex relationships between variables.
• Specifically within the context of distribution theory, considering the delta function \( \delta(x) \), applying the chain rule helps uncover how these components interact precisely.

Using the chain rule with a delta function is vital in ensuring accurate transformations and computations, which have broad implications in various fields of applied mathematics and physics. It essentially enables us to simplify and resolve the interactions between different parts of an expression and predict how a change in one part affects the whole.