Prove that Geometric mean is less than Arithmetic mean using Jensen's inequality
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So in class we solved the following exersice: State the Jensen’s Inequality for a convex function and use it to show that for a finite set of real numbers the geometric mean is less than or equal to the arithmetic mean. Unfortunately I lost my notes and cannot recall the proof at all.
The Jensen's inequality there is in Roydens book is the following:
Let $phi$ be a convex function on $mathbb{R}$, $f$ an integrable function over $[0,1]$ and $phicirc f$ integrable on $[0,1]$
Then $phi(int_0^1 fdx)=int_0^1 phicirc f dx$
This equation seems that to concern the Lebesque measure however I remember that in class to prove the result we had introduced the counting measure defined on a set. So I guess maybe the above Jensen's inequality is different than the one we used in class.
So does anyone know a proof of the result using the counting measure defined on a set and using some sort of Jensen's inequality possibly different than the one I have above?
Thanks in advance
measure-theory
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So in class we solved the following exersice: State the Jensen’s Inequality for a convex function and use it to show that for a finite set of real numbers the geometric mean is less than or equal to the arithmetic mean. Unfortunately I lost my notes and cannot recall the proof at all.
The Jensen's inequality there is in Roydens book is the following:
Let $phi$ be a convex function on $mathbb{R}$, $f$ an integrable function over $[0,1]$ and $phicirc f$ integrable on $[0,1]$
Then $phi(int_0^1 fdx)=int_0^1 phicirc f dx$
This equation seems that to concern the Lebesque measure however I remember that in class to prove the result we had introduced the counting measure defined on a set. So I guess maybe the above Jensen's inequality is different than the one we used in class.
So does anyone know a proof of the result using the counting measure defined on a set and using some sort of Jensen's inequality possibly different than the one I have above?
Thanks in advance
measure-theory
$endgroup$
add a comment |
$begingroup$
So in class we solved the following exersice: State the Jensen’s Inequality for a convex function and use it to show that for a finite set of real numbers the geometric mean is less than or equal to the arithmetic mean. Unfortunately I lost my notes and cannot recall the proof at all.
The Jensen's inequality there is in Roydens book is the following:
Let $phi$ be a convex function on $mathbb{R}$, $f$ an integrable function over $[0,1]$ and $phicirc f$ integrable on $[0,1]$
Then $phi(int_0^1 fdx)=int_0^1 phicirc f dx$
This equation seems that to concern the Lebesque measure however I remember that in class to prove the result we had introduced the counting measure defined on a set. So I guess maybe the above Jensen's inequality is different than the one we used in class.
So does anyone know a proof of the result using the counting measure defined on a set and using some sort of Jensen's inequality possibly different than the one I have above?
Thanks in advance
measure-theory
$endgroup$
So in class we solved the following exersice: State the Jensen’s Inequality for a convex function and use it to show that for a finite set of real numbers the geometric mean is less than or equal to the arithmetic mean. Unfortunately I lost my notes and cannot recall the proof at all.
The Jensen's inequality there is in Roydens book is the following:
Let $phi$ be a convex function on $mathbb{R}$, $f$ an integrable function over $[0,1]$ and $phicirc f$ integrable on $[0,1]$
Then $phi(int_0^1 fdx)=int_0^1 phicirc f dx$
This equation seems that to concern the Lebesque measure however I remember that in class to prove the result we had introduced the counting measure defined on a set. So I guess maybe the above Jensen's inequality is different than the one we used in class.
So does anyone know a proof of the result using the counting measure defined on a set and using some sort of Jensen's inequality possibly different than the one I have above?
Thanks in advance
measure-theory
measure-theory
edited Dec 4 '18 at 22:20
Masacroso
13.1k41747
13.1k41747
asked Dec 4 '18 at 22:02
TheGeometerTheGeometer
937619
937619
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2 Answers
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Forget about measure theory; I'm pretty sure your class only wants you to use the conceptually simpler result $f(sum_i w_i x_i)le sum_i w_i f(x_i)$, with non-negative $w_i$ summing to $1$. The choice $f(x):=-ln x,,w_i=frac{1}{n}$ gives $-lnfrac{1}{n}sum_ix_ile-frac{1}{n}sum_iln x_i$. Applying $xmapstoexp -x$, which reverses order, completes the proof.
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add a comment |
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The Jensen's inequality is the result of applying the inequality
$$f(x(1-t)+yt)le (1-t)f(x)+t f(y)tag1$$
for some convex function $f$ and $tin[0,1]$. This can be generalized to
$$fleft(sum_{k=1}^n a_k x_kright)lesum_{k=1}^n a_k f(x_k)tag2$$
for $sum_{k=1}^n a_k=1$ and each $a_kin[0,1]$. The inequalities above are reverted if $f$ is concave instead of convex.
This can be extended also to series and integrals, in the latter case we can derive the Jensen's inequality.
In your case, for $c_k>0$, we have that
$$frac1nsum_{k=1}^n c_kgesqrt[n]{prod_{k=1}^n c_k}iff logleft(sum_{k=1}^n frac{c_k}nright)ge sum_{k=1}^nfrac1nlog(c_k)tag3$$
what holds because $log$ is concave.
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2 Answers
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2 Answers
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$begingroup$
Forget about measure theory; I'm pretty sure your class only wants you to use the conceptually simpler result $f(sum_i w_i x_i)le sum_i w_i f(x_i)$, with non-negative $w_i$ summing to $1$. The choice $f(x):=-ln x,,w_i=frac{1}{n}$ gives $-lnfrac{1}{n}sum_ix_ile-frac{1}{n}sum_iln x_i$. Applying $xmapstoexp -x$, which reverses order, completes the proof.
$endgroup$
add a comment |
$begingroup$
Forget about measure theory; I'm pretty sure your class only wants you to use the conceptually simpler result $f(sum_i w_i x_i)le sum_i w_i f(x_i)$, with non-negative $w_i$ summing to $1$. The choice $f(x):=-ln x,,w_i=frac{1}{n}$ gives $-lnfrac{1}{n}sum_ix_ile-frac{1}{n}sum_iln x_i$. Applying $xmapstoexp -x$, which reverses order, completes the proof.
$endgroup$
add a comment |
$begingroup$
Forget about measure theory; I'm pretty sure your class only wants you to use the conceptually simpler result $f(sum_i w_i x_i)le sum_i w_i f(x_i)$, with non-negative $w_i$ summing to $1$. The choice $f(x):=-ln x,,w_i=frac{1}{n}$ gives $-lnfrac{1}{n}sum_ix_ile-frac{1}{n}sum_iln x_i$. Applying $xmapstoexp -x$, which reverses order, completes the proof.
$endgroup$
Forget about measure theory; I'm pretty sure your class only wants you to use the conceptually simpler result $f(sum_i w_i x_i)le sum_i w_i f(x_i)$, with non-negative $w_i$ summing to $1$. The choice $f(x):=-ln x,,w_i=frac{1}{n}$ gives $-lnfrac{1}{n}sum_ix_ile-frac{1}{n}sum_iln x_i$. Applying $xmapstoexp -x$, which reverses order, completes the proof.
answered Dec 4 '18 at 22:13
J.G.J.G.
28.1k22844
28.1k22844
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$begingroup$
The Jensen's inequality is the result of applying the inequality
$$f(x(1-t)+yt)le (1-t)f(x)+t f(y)tag1$$
for some convex function $f$ and $tin[0,1]$. This can be generalized to
$$fleft(sum_{k=1}^n a_k x_kright)lesum_{k=1}^n a_k f(x_k)tag2$$
for $sum_{k=1}^n a_k=1$ and each $a_kin[0,1]$. The inequalities above are reverted if $f$ is concave instead of convex.
This can be extended also to series and integrals, in the latter case we can derive the Jensen's inequality.
In your case, for $c_k>0$, we have that
$$frac1nsum_{k=1}^n c_kgesqrt[n]{prod_{k=1}^n c_k}iff logleft(sum_{k=1}^n frac{c_k}nright)ge sum_{k=1}^nfrac1nlog(c_k)tag3$$
what holds because $log$ is concave.
$endgroup$
add a comment |
$begingroup$
The Jensen's inequality is the result of applying the inequality
$$f(x(1-t)+yt)le (1-t)f(x)+t f(y)tag1$$
for some convex function $f$ and $tin[0,1]$. This can be generalized to
$$fleft(sum_{k=1}^n a_k x_kright)lesum_{k=1}^n a_k f(x_k)tag2$$
for $sum_{k=1}^n a_k=1$ and each $a_kin[0,1]$. The inequalities above are reverted if $f$ is concave instead of convex.
This can be extended also to series and integrals, in the latter case we can derive the Jensen's inequality.
In your case, for $c_k>0$, we have that
$$frac1nsum_{k=1}^n c_kgesqrt[n]{prod_{k=1}^n c_k}iff logleft(sum_{k=1}^n frac{c_k}nright)ge sum_{k=1}^nfrac1nlog(c_k)tag3$$
what holds because $log$ is concave.
$endgroup$
add a comment |
$begingroup$
The Jensen's inequality is the result of applying the inequality
$$f(x(1-t)+yt)le (1-t)f(x)+t f(y)tag1$$
for some convex function $f$ and $tin[0,1]$. This can be generalized to
$$fleft(sum_{k=1}^n a_k x_kright)lesum_{k=1}^n a_k f(x_k)tag2$$
for $sum_{k=1}^n a_k=1$ and each $a_kin[0,1]$. The inequalities above are reverted if $f$ is concave instead of convex.
This can be extended also to series and integrals, in the latter case we can derive the Jensen's inequality.
In your case, for $c_k>0$, we have that
$$frac1nsum_{k=1}^n c_kgesqrt[n]{prod_{k=1}^n c_k}iff logleft(sum_{k=1}^n frac{c_k}nright)ge sum_{k=1}^nfrac1nlog(c_k)tag3$$
what holds because $log$ is concave.
$endgroup$
The Jensen's inequality is the result of applying the inequality
$$f(x(1-t)+yt)le (1-t)f(x)+t f(y)tag1$$
for some convex function $f$ and $tin[0,1]$. This can be generalized to
$$fleft(sum_{k=1}^n a_k x_kright)lesum_{k=1}^n a_k f(x_k)tag2$$
for $sum_{k=1}^n a_k=1$ and each $a_kin[0,1]$. The inequalities above are reverted if $f$ is concave instead of convex.
This can be extended also to series and integrals, in the latter case we can derive the Jensen's inequality.
In your case, for $c_k>0$, we have that
$$frac1nsum_{k=1}^n c_kgesqrt[n]{prod_{k=1}^n c_k}iff logleft(sum_{k=1}^n frac{c_k}nright)ge sum_{k=1}^nfrac1nlog(c_k)tag3$$
what holds because $log$ is concave.
edited Dec 4 '18 at 22:52
answered Dec 4 '18 at 22:28
MasacrosoMasacroso
13.1k41747
13.1k41747
add a comment |
add a comment |
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