Is there a way to solve this convolution inequality?

In summary, the conversation is about finding functions g that satisfy a convolution inequality for any positive function v in L^1[0,T]. The inequality states that the convolution of g and v must be less than or equal to v, with * representing the convolution operation. However, it is not possible to find such functions as there are counterexamples that show the inequality cannot hold for all functions.
  • #1
amirmath
8
0
Dear friends,

I am interesting to find some functions g satisfying the following convolution inequality

(g[itex]\ast[/itex]v)(t)[itex]\leq[/itex]v(t)

for any positive function v[itex]\in[/itex]L[itex]^{1}[/itex][0,T] and * denotes the convolution between g and v.
 
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  • #2
amirmath said:
Dear friends,

I am interesting to find some functions g satisfying the following convolution inequality

(g[itex]\ast[/itex]v)(t)[itex]\leq[/itex]v(t)

for any positive function v[itex]\in[/itex]L[itex]^{1}[/itex][0,T] and * denotes the convolution between g and v.

The way you've worded the statement, it's not possible. Suppose that [itex] v \in L^1[0, T] [/itex] satisfies 0 < (g*v)(0) < v(0). Let v'(t) = v(t) for all t other than 0 and v'(0) = .5(g*v)(0). Then v = v' in the sense of L1, but (g*v)(0) > v'(0).
 

1. What is a convolution inequality?

A convolution inequality is a mathematical statement that relates two functions through a convolution operation. It expresses that the convolution of two functions is always less than or equal to the product of the maximum values of the individual functions.

2. How is a convolution inequality used in science?

A convolution inequality is used in various fields of science, including signal processing, statistics, and probability theory. It is used to analyze the behavior of systems and to solve problems involving the convolution of functions.

3. What are the key components of a convolution inequality?

The key components of a convolution inequality are the two functions involved, the convolution operation, and the maximum values of the individual functions. These components are used to express the relationship between the functions and their convolution.

4. What are the applications of a convolution inequality?

Convolution inequalities have many practical applications, such as in image processing, audio signal processing, and data analysis. They are also used in probability theory for calculating the distribution of sums of random variables.

5. How is a convolution inequality proven?

A convolution inequality is typically proven using mathematical techniques such as induction, contradiction, or direct proof. It may also be proven by using other inequalities or known properties of the functions involved.

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