Signed measure inequality |v1+v2|<=|v1|+|v2|

In summary: Substituting these expressions into our previous one, we get (ν_1 + ν_2)^+ + (ν_1 + ν_2)^- = (ν_1^+ + ν_2^+) + (ν_1^- + ν_2^-) ≤ (λ_1 + μ_1) + (λ_2 + μ_2) = |ν_1| + |ν_2|. Therefore, we have shown that |ν_1 + ν_2| ≤ |ν_1| + |ν_2|, as desired. I hope this helps you to solve the exercise. Good luck! In summary, we must use Exercise 4, which deals
  • #1
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Homework Statement


The problem is Excercise 5. in page 88 of Folland's "real analysis: modern techniques and their applications", 2nd edition, as the image below shows.
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Homework Equations


As the hint indicates, we should use Excercise 4.

The Attempt at a Solution


From Excercise 4, if signed measure [tex]\nu=\lambda-\mu[/tex], then [tex]|\nu|=\nu^++\nu^-\leq\lambda+\mu[/tex]. Supposing [tex]\nu_1=\lambda_1-\mu_1[/tex], [tex]\nu_2=\lambda_2-\mu_2[/tex], then [tex]|\nu_1+\nu_2|=(\nu_1+\nu_2)^++(\nu_1+\nu_2)^-[/tex], If we can prove [tex](\nu_1+\nu_2)^+\leq\nu_1^++\nu_1^-=|\nu_1|[/tex] and [tex](\nu_1+\nu_2)^-\leq\nu_2^++\nu_2^-=|\nu_2|[/tex], then the result is obtained. But I can not prove these inequality, please help me, thanks!
 
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  • #2




Thank you for bringing this problem to our attention. After reviewing the exercise and the hint provided, I believe I can offer some guidance on how to approach this problem.

Firstly, let's look at the given hint. It suggests using Exercise 4, which deals with the absolute value of a signed measure. Remember that the absolute value of a signed measure, denoted by |ν|, is defined as |ν| = ν^+ + ν^-, where ν^+ and ν^- are the positive and negative variations of ν, respectively.

Now, let's consider the two signed measures given in the problem, ν_1 = λ_1 - μ_1 and ν_2 = λ_2 - μ_2. We want to show that |ν_1 + ν_2| ≤ |ν_1| + |ν_2|. Using the definition of the absolute value of a signed measure, we have |ν_1 + ν_2| = (ν_1 + ν_2)^+ + (ν_1 + ν_2)^-.

To simplify this expression, we can use the properties of signed measures and the fact that ν_1 and ν_2 are both signed measures. For example, we know that (ν_1 + ν_2)^+ = ν_1^+ + ν_2^+ and (ν_1 + ν_2)^- = ν_1^- + ν_2^-. This is because the positive and negative variations of a signed measure are additive.

Now, using these properties, we can rewrite the expression as (ν_1 + ν_2)^+ + (ν_1 + ν_2)^- = (ν_1^+ + ν_2^+) + (ν_1^- + ν_2^-).

Next, we can use the given hint from Exercise 4, which states that if ν = λ - μ, then |ν| ≤ λ + μ. In our case, we have ν_1 = λ_1 - μ_1 and ν_2 = λ_2 - μ_2. So, using the hint, we can write |ν_1| = |λ_1 - μ_1| ≤ λ_1 + μ_1 and |ν_2| = |λ_2 - μ_2| ≤ λ_2 + μ_2
 

What is a signed measure inequality?

A signed measure inequality is a mathematical statement that compares the absolute values of two vectors, v1 and v2, and states that the absolute value of their sum is less than or equal to the sum of their individual absolute values. This inequality is commonly used in linear algebra and functional analysis.

Why is the signed measure inequality important?

The signed measure inequality is important because it helps us understand the relationship between the absolute values of two vectors. It is also a useful tool in proving theorems and solving mathematical problems involving vectors.

How is the signed measure inequality related to the triangle inequality?

The signed measure inequality is closely related to the triangle inequality, which states that the length of one side of a triangle must be less than or equal to the sum of the lengths of the other two sides. The signed measure inequality is a generalization of this concept to vectors in any dimension.

Can the signed measure inequality be extended to more than two vectors?

Yes, the signed measure inequality can be extended to any number of vectors. In this case, the sum of the absolute values of all the vectors must be greater than or equal to the absolute value of their sum.

Are there any exceptions to the signed measure inequality?

The signed measure inequality is generally true for all vectors in a vector space. However, there are some exceptions, such as when dealing with complex numbers or infinite-dimensional vector spaces. In these cases, the inequality may need to be modified or replaced with a different inequality.

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