Complex variables: ML inequality

In summary, the proof of the ML inequality from complex analysis states that if f(z) is continuous on a contour C, then |integral of f(z) dz| is less than or equal to the integral of |f(z)| times |z'(t)|. The text states that since |f| is bounded on C with an upper bound of M, the integral is also bounded by M. This is because M is an upper bound for both |f(z)| and |f(z(t))|.
  • #1
futurebird
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I'm studying the proof of the [tex]ML[/tex] inequlity from complex analysis. I don't know what they did in one step of the proof and I was wondering if anyone can explain the step to me.

First of all the theorem says:

Let f(z) be continuous on a contour C. Then

[tex]|\int_{c}f(z)dz| \leq ML[/tex]

Where [tex]L[/tex] is the length of C and [tex]M[/tex] is an upper bound for |f| on C.

PROOF:
After a few steps we have:

[tex]|\int_{c}f(z)dz| \leq \int^{b}_{a} |f(z(t))||z'(t)|dt[/tex]

And the text says " |f| is bounded on C, [tex]|f(z)| \leq M[/tex] on C, where [tex]M[/tex] is constant, then:

[tex]|\int_{c}f(z)dz| \leq M\int^{b}_{a} |z'(t)|dt[/tex]

How did they get the [tex]M[/tex] out of the integral?
 
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  • #2
[tex]\int^{b}_{a} |f(z(t))||z'(t)|dt \leq \int_a^b M|z'(t)|dt = M \int_a^b |z'(t)|dt [/tex]
 
  • #3
So it's okay that M is an upper bound for |f(z)|, that's the same thing as an upper bound for |f(z(t))|? Okay thanks, I think I get it now.
 

Related to Complex variables: ML inequality

1. What is the ML inequality in complex variables?

The ML inequality, also known as the Cauchy estimate, is a mathematical tool used in complex analysis to bound the value of a function along a contour. It states that if a function f is holomorphic within and on a closed contour C, and the absolute value of f is bounded by M on C, then the maximum value of f on C is less than or equal to M times the length of C.

2. How is the ML inequality used in complex variables?

The ML inequality is used to estimate the maximum value of a holomorphic function along a contour, which can be useful in proving the existence of certain complex-valued integrals and in solving problems involving power series and Laurent series.

3. Can the ML inequality be extended to include more than one contour?

Yes, the ML inequality can be extended to include multiple contours by using the triangle inequality. This allows for more complex integrals to be estimated using the ML inequality.

4. Is the ML inequality only applicable to holomorphic functions?

Yes, the ML inequality is only applicable to holomorphic functions, which are functions that are complex differentiable at every point within a given domain. This means that the function must have a well-defined derivative at every point in its domain.

5. What are some real-world applications of the ML inequality?

The ML inequality has various applications in engineering, physics, and economics. It is commonly used in signal processing and control theory to analyze the stability of systems. It is also used in calculating the value of certain integrals in physics and in solving optimization problems in economics.

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