Generalize Jensen's Integral Inequality to the product of two functions
up vote
0
down vote
favorite
Let $E$ be a measurable set with $m(E)>0$.
Let $f$, $gamma$ be two measurable, real-valued function which are finite a.e. on $E$ with $f, gamma$ and $fcdot gamma$ all integrable.
Assume $gammageq 0$ and $int_{E}gamma>0$. Then if $phi$ is a convex function on an open interval $(a,b)$ containing the range of $f$, then we have
$phiBig(dfrac{int_{E}fcdotgamma}{int_{E}gamma}Big)leqBig(dfrac{int_{E}phi(f)cdotgamma}{int_{E}gamma}Big)$
I am trying to modify the proof of the simpler version (the one with only $f$) of Jensen's inequality to this more general version, but I don't really know how to deal with the new function $gamma$.
Any hints or detailed explanations are really appreciated!!
${bf Edit 1:}$
I got some but still limited development.
To make things simpler, I assume $phi$ has $1^{st}$ derivative, and assume $int_{E}gamma=1$
As $phi$ is convex, for each $a$, we have $phi(y)>(y-a)phi'(a)+phi(a)$
so in particular, as $gammageq 0$, we have
$int phibig(f(x)big)cdotgamma(x)dxgeqintbig[(f(x)-a)phi'(a)+phi(a)big]gamma(x)dx=int big(f(x)-abig)gamma(x)dxtimes phi'(a)+phi(a)$
Now, let $a=int f(x)cdotgamma(x)dx$, we will have $int big(f(x)-abig)gamma(x)dx=0$ and we are done in this simple case.
In a general case, a convex function on the real line has only right derivative, and thus we have $phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$, so with the complicated notation, our proof is still okay.
However, how do I expand this case to the case where $int_{E}gamma$ is not 1?
$textbf{My Completed Proof:}$
Okay, I think I proved it.
To make things simpler, let $int_{E}gamma=bgeq 0$ and suppose $phi(x)$ has the first derivative.
As $phi$ is convex, for each $a$, we have $$phi(y)>(y-a)phi'(a)+phi(a)$$
So in particular, as $gammageq 0$, we have:
$dfrac{int_{E}phi(f(x))gamma(x)dx}{b}geqdfrac{int_{E} [(f(x)-a)phi'(a)+phi(a)]gamma(x)dx}{b}=dfrac{phi'(a)}{b}int_{E}f(x)gamma(x)dx-aphi'(a)+phi(a)$
Now, let $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$, the first two summands of the above equation will be killed, and we get what we want.
So in general case, convex function has only right derivatives, so we have $$phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$$
By letting $F(a)=lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}$ our proof is still valid, since $F(a)$ will be killed in the end as what happened to $phi'(x)$, then we are done.
Is my proof okay here? Feel free to point out my mistake or better proof!
Thank you!
proof-verification convex-analysis lebesgue-integral jensen-inequality
add a comment |
up vote
0
down vote
favorite
Let $E$ be a measurable set with $m(E)>0$.
Let $f$, $gamma$ be two measurable, real-valued function which are finite a.e. on $E$ with $f, gamma$ and $fcdot gamma$ all integrable.
Assume $gammageq 0$ and $int_{E}gamma>0$. Then if $phi$ is a convex function on an open interval $(a,b)$ containing the range of $f$, then we have
$phiBig(dfrac{int_{E}fcdotgamma}{int_{E}gamma}Big)leqBig(dfrac{int_{E}phi(f)cdotgamma}{int_{E}gamma}Big)$
I am trying to modify the proof of the simpler version (the one with only $f$) of Jensen's inequality to this more general version, but I don't really know how to deal with the new function $gamma$.
Any hints or detailed explanations are really appreciated!!
${bf Edit 1:}$
I got some but still limited development.
To make things simpler, I assume $phi$ has $1^{st}$ derivative, and assume $int_{E}gamma=1$
As $phi$ is convex, for each $a$, we have $phi(y)>(y-a)phi'(a)+phi(a)$
so in particular, as $gammageq 0$, we have
$int phibig(f(x)big)cdotgamma(x)dxgeqintbig[(f(x)-a)phi'(a)+phi(a)big]gamma(x)dx=int big(f(x)-abig)gamma(x)dxtimes phi'(a)+phi(a)$
Now, let $a=int f(x)cdotgamma(x)dx$, we will have $int big(f(x)-abig)gamma(x)dx=0$ and we are done in this simple case.
In a general case, a convex function on the real line has only right derivative, and thus we have $phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$, so with the complicated notation, our proof is still okay.
However, how do I expand this case to the case where $int_{E}gamma$ is not 1?
$textbf{My Completed Proof:}$
Okay, I think I proved it.
To make things simpler, let $int_{E}gamma=bgeq 0$ and suppose $phi(x)$ has the first derivative.
As $phi$ is convex, for each $a$, we have $$phi(y)>(y-a)phi'(a)+phi(a)$$
So in particular, as $gammageq 0$, we have:
$dfrac{int_{E}phi(f(x))gamma(x)dx}{b}geqdfrac{int_{E} [(f(x)-a)phi'(a)+phi(a)]gamma(x)dx}{b}=dfrac{phi'(a)}{b}int_{E}f(x)gamma(x)dx-aphi'(a)+phi(a)$
Now, let $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$, the first two summands of the above equation will be killed, and we get what we want.
So in general case, convex function has only right derivatives, so we have $$phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$$
By letting $F(a)=lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}$ our proof is still valid, since $F(a)$ will be killed in the end as what happened to $phi'(x)$, then we are done.
Is my proof okay here? Feel free to point out my mistake or better proof!
Thank you!
proof-verification convex-analysis lebesgue-integral jensen-inequality
Why don't you define $P(A) = dfrac{1}{int_E gamma} int_{A cap E} gamma$ so that $P$ is a probability measure and what you want to prove is just Jensen's inequality?
– Will M.
Nov 15 at 1:35
@WillM. I haven't learnt materials about probability measure, would you mind making it more specific?
– JacobsonRadical
Nov 15 at 1:40
I am confused, do you know or not know about Jensen's inequality?
– Will M.
Nov 15 at 1:40
@WillM. My professor only talks about Jensen's Inequality in the context of Lebesgue Integral, without talking about probability measure. So... I don't know whether I know or do not know Jensen's inequality...
– JacobsonRadical
Nov 15 at 1:43
Jensen's inequality only holds in probability spaces. en.wikipedia.org/wiki/Jensen%27s_inequality
– Will M.
Nov 15 at 3:06
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
Let $E$ be a measurable set with $m(E)>0$.
Let $f$, $gamma$ be two measurable, real-valued function which are finite a.e. on $E$ with $f, gamma$ and $fcdot gamma$ all integrable.
Assume $gammageq 0$ and $int_{E}gamma>0$. Then if $phi$ is a convex function on an open interval $(a,b)$ containing the range of $f$, then we have
$phiBig(dfrac{int_{E}fcdotgamma}{int_{E}gamma}Big)leqBig(dfrac{int_{E}phi(f)cdotgamma}{int_{E}gamma}Big)$
I am trying to modify the proof of the simpler version (the one with only $f$) of Jensen's inequality to this more general version, but I don't really know how to deal with the new function $gamma$.
Any hints or detailed explanations are really appreciated!!
${bf Edit 1:}$
I got some but still limited development.
To make things simpler, I assume $phi$ has $1^{st}$ derivative, and assume $int_{E}gamma=1$
As $phi$ is convex, for each $a$, we have $phi(y)>(y-a)phi'(a)+phi(a)$
so in particular, as $gammageq 0$, we have
$int phibig(f(x)big)cdotgamma(x)dxgeqintbig[(f(x)-a)phi'(a)+phi(a)big]gamma(x)dx=int big(f(x)-abig)gamma(x)dxtimes phi'(a)+phi(a)$
Now, let $a=int f(x)cdotgamma(x)dx$, we will have $int big(f(x)-abig)gamma(x)dx=0$ and we are done in this simple case.
In a general case, a convex function on the real line has only right derivative, and thus we have $phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$, so with the complicated notation, our proof is still okay.
However, how do I expand this case to the case where $int_{E}gamma$ is not 1?
$textbf{My Completed Proof:}$
Okay, I think I proved it.
To make things simpler, let $int_{E}gamma=bgeq 0$ and suppose $phi(x)$ has the first derivative.
As $phi$ is convex, for each $a$, we have $$phi(y)>(y-a)phi'(a)+phi(a)$$
So in particular, as $gammageq 0$, we have:
$dfrac{int_{E}phi(f(x))gamma(x)dx}{b}geqdfrac{int_{E} [(f(x)-a)phi'(a)+phi(a)]gamma(x)dx}{b}=dfrac{phi'(a)}{b}int_{E}f(x)gamma(x)dx-aphi'(a)+phi(a)$
Now, let $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$, the first two summands of the above equation will be killed, and we get what we want.
So in general case, convex function has only right derivatives, so we have $$phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$$
By letting $F(a)=lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}$ our proof is still valid, since $F(a)$ will be killed in the end as what happened to $phi'(x)$, then we are done.
Is my proof okay here? Feel free to point out my mistake or better proof!
Thank you!
proof-verification convex-analysis lebesgue-integral jensen-inequality
Let $E$ be a measurable set with $m(E)>0$.
Let $f$, $gamma$ be two measurable, real-valued function which are finite a.e. on $E$ with $f, gamma$ and $fcdot gamma$ all integrable.
Assume $gammageq 0$ and $int_{E}gamma>0$. Then if $phi$ is a convex function on an open interval $(a,b)$ containing the range of $f$, then we have
$phiBig(dfrac{int_{E}fcdotgamma}{int_{E}gamma}Big)leqBig(dfrac{int_{E}phi(f)cdotgamma}{int_{E}gamma}Big)$
I am trying to modify the proof of the simpler version (the one with only $f$) of Jensen's inequality to this more general version, but I don't really know how to deal with the new function $gamma$.
Any hints or detailed explanations are really appreciated!!
${bf Edit 1:}$
I got some but still limited development.
To make things simpler, I assume $phi$ has $1^{st}$ derivative, and assume $int_{E}gamma=1$
As $phi$ is convex, for each $a$, we have $phi(y)>(y-a)phi'(a)+phi(a)$
so in particular, as $gammageq 0$, we have
$int phibig(f(x)big)cdotgamma(x)dxgeqintbig[(f(x)-a)phi'(a)+phi(a)big]gamma(x)dx=int big(f(x)-abig)gamma(x)dxtimes phi'(a)+phi(a)$
Now, let $a=int f(x)cdotgamma(x)dx$, we will have $int big(f(x)-abig)gamma(x)dx=0$ and we are done in this simple case.
In a general case, a convex function on the real line has only right derivative, and thus we have $phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$, so with the complicated notation, our proof is still okay.
However, how do I expand this case to the case where $int_{E}gamma$ is not 1?
$textbf{My Completed Proof:}$
Okay, I think I proved it.
To make things simpler, let $int_{E}gamma=bgeq 0$ and suppose $phi(x)$ has the first derivative.
As $phi$ is convex, for each $a$, we have $$phi(y)>(y-a)phi'(a)+phi(a)$$
So in particular, as $gammageq 0$, we have:
$dfrac{int_{E}phi(f(x))gamma(x)dx}{b}geqdfrac{int_{E} [(f(x)-a)phi'(a)+phi(a)]gamma(x)dx}{b}=dfrac{phi'(a)}{b}int_{E}f(x)gamma(x)dx-aphi'(a)+phi(a)$
Now, let $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$, the first two summands of the above equation will be killed, and we get what we want.
So in general case, convex function has only right derivatives, so we have $$phi(y)>(y-a)lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}+phi(a)$$
By letting $F(a)=lim_{hrightarrow 0^{+}}dfrac{phi(a+h)-phi(a)}{h}$ our proof is still valid, since $F(a)$ will be killed in the end as what happened to $phi'(x)$, then we are done.
Is my proof okay here? Feel free to point out my mistake or better proof!
Thank you!
proof-verification convex-analysis lebesgue-integral jensen-inequality
proof-verification convex-analysis lebesgue-integral jensen-inequality
edited Nov 15 at 2:14
asked Nov 15 at 0:45
JacobsonRadical
393111
393111
Why don't you define $P(A) = dfrac{1}{int_E gamma} int_{A cap E} gamma$ so that $P$ is a probability measure and what you want to prove is just Jensen's inequality?
– Will M.
Nov 15 at 1:35
@WillM. I haven't learnt materials about probability measure, would you mind making it more specific?
– JacobsonRadical
Nov 15 at 1:40
I am confused, do you know or not know about Jensen's inequality?
– Will M.
Nov 15 at 1:40
@WillM. My professor only talks about Jensen's Inequality in the context of Lebesgue Integral, without talking about probability measure. So... I don't know whether I know or do not know Jensen's inequality...
– JacobsonRadical
Nov 15 at 1:43
Jensen's inequality only holds in probability spaces. en.wikipedia.org/wiki/Jensen%27s_inequality
– Will M.
Nov 15 at 3:06
add a comment |
Why don't you define $P(A) = dfrac{1}{int_E gamma} int_{A cap E} gamma$ so that $P$ is a probability measure and what you want to prove is just Jensen's inequality?
– Will M.
Nov 15 at 1:35
@WillM. I haven't learnt materials about probability measure, would you mind making it more specific?
– JacobsonRadical
Nov 15 at 1:40
I am confused, do you know or not know about Jensen's inequality?
– Will M.
Nov 15 at 1:40
@WillM. My professor only talks about Jensen's Inequality in the context of Lebesgue Integral, without talking about probability measure. So... I don't know whether I know or do not know Jensen's inequality...
– JacobsonRadical
Nov 15 at 1:43
Jensen's inequality only holds in probability spaces. en.wikipedia.org/wiki/Jensen%27s_inequality
– Will M.
Nov 15 at 3:06
Why don't you define $P(A) = dfrac{1}{int_E gamma} int_{A cap E} gamma$ so that $P$ is a probability measure and what you want to prove is just Jensen's inequality?
– Will M.
Nov 15 at 1:35
Why don't you define $P(A) = dfrac{1}{int_E gamma} int_{A cap E} gamma$ so that $P$ is a probability measure and what you want to prove is just Jensen's inequality?
– Will M.
Nov 15 at 1:35
@WillM. I haven't learnt materials about probability measure, would you mind making it more specific?
– JacobsonRadical
Nov 15 at 1:40
@WillM. I haven't learnt materials about probability measure, would you mind making it more specific?
– JacobsonRadical
Nov 15 at 1:40
I am confused, do you know or not know about Jensen's inequality?
– Will M.
Nov 15 at 1:40
I am confused, do you know or not know about Jensen's inequality?
– Will M.
Nov 15 at 1:40
@WillM. My professor only talks about Jensen's Inequality in the context of Lebesgue Integral, without talking about probability measure. So... I don't know whether I know or do not know Jensen's inequality...
– JacobsonRadical
Nov 15 at 1:43
@WillM. My professor only talks about Jensen's Inequality in the context of Lebesgue Integral, without talking about probability measure. So... I don't know whether I know or do not know Jensen's inequality...
– JacobsonRadical
Nov 15 at 1:43
Jensen's inequality only holds in probability spaces. en.wikipedia.org/wiki/Jensen%27s_inequality
– Will M.
Nov 15 at 3:06
Jensen's inequality only holds in probability spaces. en.wikipedia.org/wiki/Jensen%27s_inequality
– Will M.
Nov 15 at 3:06
add a comment |
1 Answer
1
active
oldest
votes
up vote
0
down vote
accepted
Most of the answer is in my post, $textbf{ My Completed Proof}$ part. Only three minor points need to be added.
Firstly, in the third line of my proof, we need to comment that $a$ in is the domain of $phi$.
Secondly, in the sixth line, we need to note that $phi(f(x))$ makes sense since by hypothesis, the domain of $phi$ contains the range of $f(x)$.
Finally, $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$ makes sense because $gammageq 0$, so we can enlarge or reduce $f$ to its max or min so that $a$ is in the range of $f(x)$.
With all those details added, the proof in my post is complete.
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
Most of the answer is in my post, $textbf{ My Completed Proof}$ part. Only three minor points need to be added.
Firstly, in the third line of my proof, we need to comment that $a$ in is the domain of $phi$.
Secondly, in the sixth line, we need to note that $phi(f(x))$ makes sense since by hypothesis, the domain of $phi$ contains the range of $f(x)$.
Finally, $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$ makes sense because $gammageq 0$, so we can enlarge or reduce $f$ to its max or min so that $a$ is in the range of $f(x)$.
With all those details added, the proof in my post is complete.
add a comment |
up vote
0
down vote
accepted
Most of the answer is in my post, $textbf{ My Completed Proof}$ part. Only three minor points need to be added.
Firstly, in the third line of my proof, we need to comment that $a$ in is the domain of $phi$.
Secondly, in the sixth line, we need to note that $phi(f(x))$ makes sense since by hypothesis, the domain of $phi$ contains the range of $f(x)$.
Finally, $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$ makes sense because $gammageq 0$, so we can enlarge or reduce $f$ to its max or min so that $a$ is in the range of $f(x)$.
With all those details added, the proof in my post is complete.
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
Most of the answer is in my post, $textbf{ My Completed Proof}$ part. Only three minor points need to be added.
Firstly, in the third line of my proof, we need to comment that $a$ in is the domain of $phi$.
Secondly, in the sixth line, we need to note that $phi(f(x))$ makes sense since by hypothesis, the domain of $phi$ contains the range of $f(x)$.
Finally, $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$ makes sense because $gammageq 0$, so we can enlarge or reduce $f$ to its max or min so that $a$ is in the range of $f(x)$.
With all those details added, the proof in my post is complete.
Most of the answer is in my post, $textbf{ My Completed Proof}$ part. Only three minor points need to be added.
Firstly, in the third line of my proof, we need to comment that $a$ in is the domain of $phi$.
Secondly, in the sixth line, we need to note that $phi(f(x))$ makes sense since by hypothesis, the domain of $phi$ contains the range of $f(x)$.
Finally, $a=dfrac{int_{E}f(x)gamma(x)dx}{b}$ makes sense because $gammageq 0$, so we can enlarge or reduce $f$ to its max or min so that $a$ is in the range of $f(x)$.
With all those details added, the proof in my post is complete.
answered Nov 16 at 5:33
JacobsonRadical
393111
393111
add a comment |
add a comment |
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f2999028%2fgeneralize-jensens-integral-inequality-to-the-product-of-two-functions%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Why don't you define $P(A) = dfrac{1}{int_E gamma} int_{A cap E} gamma$ so that $P$ is a probability measure and what you want to prove is just Jensen's inequality?
– Will M.
Nov 15 at 1:35
@WillM. I haven't learnt materials about probability measure, would you mind making it more specific?
– JacobsonRadical
Nov 15 at 1:40
I am confused, do you know or not know about Jensen's inequality?
– Will M.
Nov 15 at 1:40
@WillM. My professor only talks about Jensen's Inequality in the context of Lebesgue Integral, without talking about probability measure. So... I don't know whether I know or do not know Jensen's inequality...
– JacobsonRadical
Nov 15 at 1:43
Jensen's inequality only holds in probability spaces. en.wikipedia.org/wiki/Jensen%27s_inequality
– Will M.
Nov 15 at 3:06