Proving Inequality Using Jensen's Inequality

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In summary, the conversation discusses using Jensen's inequality to prove an inequality involving integrals and a given function. The goal is to use Jensen's inequality by rewriting the left hand side or using a previous inequality. However, the issue is that the TeX formatting is not working, making it difficult to follow the conversation.
  • #1
ReyChiquito
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Let [itex]x \in \mathbb{R}^n[/itex] and

[tex]u_0>0, \qquad \int\limits_\Omega u_0(x) dx =1, \qquad E(t)=\int\limits_\Omega u(x,t)u_0(x)dx[/tex]

Im having trouble proving the following inequality

[tex]\int\limits_\Omega \frac{u_0(x)}{(1+u(x,t))^2}dx \ge \dfrac{1}{(1+E)^2}. \qquad \hbox{(1)}[/tex]

I know i have to use Jensen's inequality

[tex] f\left(\frac{1}{|\Omega|}\int\limits_\Omega u dx \right) \le \frac{1}{|\Omega|}\int\limits_\Omega f(u) dx [/tex],

where [itex]f(u)[/itex] is convex.

But in order to use it to prove (1), I need to rewrite the left hand side of the equation or use a previous inequality right?

There is where I am stuck. Can anybody give me a sugestion pls?
 
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  • #2
Is it just me or nobody can see the TeX?
 
  • #3
I cannot either.
 
  • #4
Well, first of all, it would be nice if someone tell me why the TeX doesn't work. Second of all, i got it, so nevermind.

[tex]\int[/tex]

[tex]\Omega[/tex]

[tex]\omega[/tex]

no \int? nice...
 
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FAQ: Proving Inequality Using Jensen's Inequality

What is Jensen's Inequality?

Jensen's Inequality is a mathematical concept that states that the convex function of the expected value of a random variable is less than or equal to the expected value of the convex function of the random variable.

How is Jensen's Inequality used in science?

Jensen's Inequality is often used in statistics and economics to prove important theorems and to analyze data. It is also used in other areas of science, such as physics and biology, to model and predict phenomena.

What are some real-life applications of Jensen's Inequality?

Jensen's Inequality can be used in various real-life scenarios, such as portfolio optimization in finance, analyzing health data in medical research, and predicting weather patterns in meteorology.

What is the difference between Jensen's Inequality and the Mean Value Theorem?

The Mean Value Theorem states that there exists a point in an interval where the slope of a function is equal to the average slope of the function over the interval. In contrast, Jensen's Inequality is a more general concept that applies to convex functions and random variables.

Are there any limitations to Jensen's Inequality?

Yes, Jensen's Inequality only applies to convex functions and may not hold for non-convex functions. It also assumes that the random variable has a finite expected value and that the convex function is defined over the entire range of the random variable.

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