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










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    1












    $begingroup$


    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










    share|cite|improve this question











    $endgroup$















      1












      1








      1





      $begingroup$


      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










      share|cite|improve this question











      $endgroup$




      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






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      edited Dec 4 '18 at 22:20









      Masacroso

      13.1k41747




      13.1k41747










      asked Dec 4 '18 at 22:02









      TheGeometerTheGeometer

      937619




      937619






















          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.






          share|cite|improve this answer









          $endgroup$





















            1












            $begingroup$

            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.






            share|cite|improve this answer











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              2 Answers
              2






              active

              oldest

              votes








              2 Answers
              2






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              3












              $begingroup$

              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.






              share|cite|improve this answer









              $endgroup$


















                3












                $begingroup$

                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.






                share|cite|improve this answer









                $endgroup$
















                  3












                  3








                  3





                  $begingroup$

                  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.






                  share|cite|improve this answer









                  $endgroup$



                  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.







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered Dec 4 '18 at 22:13









                  J.G.J.G.

                  28.1k22844




                  28.1k22844























                      1












                      $begingroup$

                      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.






                      share|cite|improve this answer











                      $endgroup$


















                        1












                        $begingroup$

                        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.






                        share|cite|improve this answer











                        $endgroup$
















                          1












                          1








                          1





                          $begingroup$

                          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.






                          share|cite|improve this answer











                          $endgroup$



                          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.







                          share|cite|improve this answer














                          share|cite|improve this answer



                          share|cite|improve this answer








                          edited Dec 4 '18 at 22:52

























                          answered Dec 4 '18 at 22:28









                          MasacrosoMasacroso

                          13.1k41747




                          13.1k41747






























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