Prove that a product's limit converges to a sum

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I accidentally discovered this equality, in which I can prove numerically using python.



$$lim_ktoinfty sqrt[n] prod_i=1^n(x_i+k) - k = fracsum_i=1^nx_in$$



But I need to prove this equality in an algebraic way and I could not get anywhere. The right-hand side is nothing more than an arithmetic mean, while the left-hand side is a modification of the geometric mean.
I hope someone can help me at this point.










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    up vote
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    favorite












    I accidentally discovered this equality, in which I can prove numerically using python.



    $$lim_ktoinfty sqrt[n] prod_i=1^n(x_i+k) - k = fracsum_i=1^nx_in$$



    But I need to prove this equality in an algebraic way and I could not get anywhere. The right-hand side is nothing more than an arithmetic mean, while the left-hand side is a modification of the geometric mean.
    I hope someone can help me at this point.










    share|cite|improve this question









    New contributor




    Gustavo Ale is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.





















      up vote
      3
      down vote

      favorite









      up vote
      3
      down vote

      favorite











      I accidentally discovered this equality, in which I can prove numerically using python.



      $$lim_ktoinfty sqrt[n] prod_i=1^n(x_i+k) - k = fracsum_i=1^nx_in$$



      But I need to prove this equality in an algebraic way and I could not get anywhere. The right-hand side is nothing more than an arithmetic mean, while the left-hand side is a modification of the geometric mean.
      I hope someone can help me at this point.










      share|cite|improve this question









      New contributor




      Gustavo Ale is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I accidentally discovered this equality, in which I can prove numerically using python.



      $$lim_ktoinfty sqrt[n] prod_i=1^n(x_i+k) - k = fracsum_i=1^nx_in$$



      But I need to prove this equality in an algebraic way and I could not get anywhere. The right-hand side is nothing more than an arithmetic mean, while the left-hand side is a modification of the geometric mean.
      I hope someone can help me at this point.







      real-analysis discrete-mathematics means






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      Gustavo Ale is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      edited 2 hours ago





















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      asked 3 hours ago









      Gustavo Ale

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      Gustavo Ale is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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          3 Answers
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          Assume that $k$ is greater than twice $|x_1|+ldots+|x_n|$ and also greater than twice $M=x_1^2+ldots+x_n^2$. Over the interval $left(-frac12,frac12right)$ we have
          $$ e^x = 1+x+C(x)x^2, qquad log(1+x)=x+D(x)x^2 $$
          with $|C(x)|,|D(x)|leq 1$. It follows that



          $$begineqnarray*textGM(x_1+k,ldots,x_n+k)&=&kcdot textGMleft(1+tfracx_1k,ldots,1+tfracx_nkright)\&=&kexpleft[frac1nsum_j=1^nlogleft(1+fracx_jkright)right]\&=&kexpleft[frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]\&=&kleft[1+frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]endeqnarray*$$
          and the claim is proved. It looks very reasonable also without a formal proof: the magnitude of the difference between the arithmetic mean and the geometric mean is controlled by $fractextVar(x_1,ldots,x_n)textAM(x_1,ldots,x_n)$. A translation towards the right leaves the variance unchanged and increases the mean.






          share|cite|improve this answer



























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            You could even get an interesting asymptotics considering $$y_n=sqrt[n] prod_i=1^n(x_i+k)$$ $$ log(y_n)=frac 1n sum_i=1^nlogleft((x_i+k)right)=frac 1n sum_i=1^nleft(log(k)+logleft(1+frac x_ikright)right)$$ Assuming that $x_1<x_2<cdots < x_n ll k$, use the Taylor expansion of $logleft(1+frac x_ikright)$. Then continuing with Taylor series
            $$y_n=e^log(y_n)implies
            y_n=k+fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$
            making
            $$ sqrt[n] prod_i=1^n(x_i+k) - k =fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$



            For example, using $n=10$, $k=1000$ and $x_i=p_i$, the approximation would give $frac2572671200000approx 12.8634$ while the exact calculation would lead to $approx 12.8639$






            share|cite|improve this answer


















            • 1




              @GustavoAle. For sure ! I just wanted to see what would be the asymptotics. Cheers.
              – Claude Leibovici
              24 mins ago










            • At this point the equality is proven by applying the limit itself,$$lim_ktoinftysqrt[n] prod_i=1^n(x_i+k) - k = lim_ktoinftyfracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$ where $$lim_ktoinftyfracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k + Oleft(frac1k^2right) to 0$$ remaining only $$fracsum_i=1^n x_in$$
              – Gustavo Ale
              23 mins ago

















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            1
            down vote













            Your expression under limit can be written as $k(a-b) $ and further $a-b=(a^n-b^n) / c$ where $a, b$ tend to $1$ and $c$ tends to $n$ as $ktoinfty$. And $$a^n-b^n=prod_i=1^nleft(1+fracx_ikright)-1$$ I hope you can continue it from here.






            share|cite|improve this answer




















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              3 Answers
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              3 Answers
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              up vote
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              down vote













              Assume that $k$ is greater than twice $|x_1|+ldots+|x_n|$ and also greater than twice $M=x_1^2+ldots+x_n^2$. Over the interval $left(-frac12,frac12right)$ we have
              $$ e^x = 1+x+C(x)x^2, qquad log(1+x)=x+D(x)x^2 $$
              with $|C(x)|,|D(x)|leq 1$. It follows that



              $$begineqnarray*textGM(x_1+k,ldots,x_n+k)&=&kcdot textGMleft(1+tfracx_1k,ldots,1+tfracx_nkright)\&=&kexpleft[frac1nsum_j=1^nlogleft(1+fracx_jkright)right]\&=&kexpleft[frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]\&=&kleft[1+frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]endeqnarray*$$
              and the claim is proved. It looks very reasonable also without a formal proof: the magnitude of the difference between the arithmetic mean and the geometric mean is controlled by $fractextVar(x_1,ldots,x_n)textAM(x_1,ldots,x_n)$. A translation towards the right leaves the variance unchanged and increases the mean.






              share|cite|improve this answer
























                up vote
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                Assume that $k$ is greater than twice $|x_1|+ldots+|x_n|$ and also greater than twice $M=x_1^2+ldots+x_n^2$. Over the interval $left(-frac12,frac12right)$ we have
                $$ e^x = 1+x+C(x)x^2, qquad log(1+x)=x+D(x)x^2 $$
                with $|C(x)|,|D(x)|leq 1$. It follows that



                $$begineqnarray*textGM(x_1+k,ldots,x_n+k)&=&kcdot textGMleft(1+tfracx_1k,ldots,1+tfracx_nkright)\&=&kexpleft[frac1nsum_j=1^nlogleft(1+fracx_jkright)right]\&=&kexpleft[frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]\&=&kleft[1+frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]endeqnarray*$$
                and the claim is proved. It looks very reasonable also without a formal proof: the magnitude of the difference between the arithmetic mean and the geometric mean is controlled by $fractextVar(x_1,ldots,x_n)textAM(x_1,ldots,x_n)$. A translation towards the right leaves the variance unchanged and increases the mean.






                share|cite|improve this answer






















                  up vote
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                  up vote
                  3
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                  Assume that $k$ is greater than twice $|x_1|+ldots+|x_n|$ and also greater than twice $M=x_1^2+ldots+x_n^2$. Over the interval $left(-frac12,frac12right)$ we have
                  $$ e^x = 1+x+C(x)x^2, qquad log(1+x)=x+D(x)x^2 $$
                  with $|C(x)|,|D(x)|leq 1$. It follows that



                  $$begineqnarray*textGM(x_1+k,ldots,x_n+k)&=&kcdot textGMleft(1+tfracx_1k,ldots,1+tfracx_nkright)\&=&kexpleft[frac1nsum_j=1^nlogleft(1+fracx_jkright)right]\&=&kexpleft[frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]\&=&kleft[1+frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]endeqnarray*$$
                  and the claim is proved. It looks very reasonable also without a formal proof: the magnitude of the difference between the arithmetic mean and the geometric mean is controlled by $fractextVar(x_1,ldots,x_n)textAM(x_1,ldots,x_n)$. A translation towards the right leaves the variance unchanged and increases the mean.






                  share|cite|improve this answer












                  Assume that $k$ is greater than twice $|x_1|+ldots+|x_n|$ and also greater than twice $M=x_1^2+ldots+x_n^2$. Over the interval $left(-frac12,frac12right)$ we have
                  $$ e^x = 1+x+C(x)x^2, qquad log(1+x)=x+D(x)x^2 $$
                  with $|C(x)|,|D(x)|leq 1$. It follows that



                  $$begineqnarray*textGM(x_1+k,ldots,x_n+k)&=&kcdot textGMleft(1+tfracx_1k,ldots,1+tfracx_nkright)\&=&kexpleft[frac1nsum_j=1^nlogleft(1+fracx_jkright)right]\&=&kexpleft[frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]\&=&kleft[1+frac1ksum_j=1^nfracx_jn+Thetaleft(fracMnk^2right)right]endeqnarray*$$
                  and the claim is proved. It looks very reasonable also without a formal proof: the magnitude of the difference between the arithmetic mean and the geometric mean is controlled by $fractextVar(x_1,ldots,x_n)textAM(x_1,ldots,x_n)$. A translation towards the right leaves the variance unchanged and increases the mean.







                  share|cite|improve this answer












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                  share|cite|improve this answer










                  answered 2 hours ago









                  Jack D'Aurizio

                  280k33272651




                  280k33272651




















                      up vote
                      2
                      down vote













                      You could even get an interesting asymptotics considering $$y_n=sqrt[n] prod_i=1^n(x_i+k)$$ $$ log(y_n)=frac 1n sum_i=1^nlogleft((x_i+k)right)=frac 1n sum_i=1^nleft(log(k)+logleft(1+frac x_ikright)right)$$ Assuming that $x_1<x_2<cdots < x_n ll k$, use the Taylor expansion of $logleft(1+frac x_ikright)$. Then continuing with Taylor series
                      $$y_n=e^log(y_n)implies
                      y_n=k+fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$
                      making
                      $$ sqrt[n] prod_i=1^n(x_i+k) - k =fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$



                      For example, using $n=10$, $k=1000$ and $x_i=p_i$, the approximation would give $frac2572671200000approx 12.8634$ while the exact calculation would lead to $approx 12.8639$






                      share|cite|improve this answer


















                      • 1




                        @GustavoAle. For sure ! I just wanted to see what would be the asymptotics. Cheers.
                        – Claude Leibovici
                        24 mins ago










                      • At this point the equality is proven by applying the limit itself,$$lim_ktoinftysqrt[n] prod_i=1^n(x_i+k) - k = lim_ktoinftyfracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$ where $$lim_ktoinftyfracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k + Oleft(frac1k^2right) to 0$$ remaining only $$fracsum_i=1^n x_in$$
                        – Gustavo Ale
                        23 mins ago














                      up vote
                      2
                      down vote













                      You could even get an interesting asymptotics considering $$y_n=sqrt[n] prod_i=1^n(x_i+k)$$ $$ log(y_n)=frac 1n sum_i=1^nlogleft((x_i+k)right)=frac 1n sum_i=1^nleft(log(k)+logleft(1+frac x_ikright)right)$$ Assuming that $x_1<x_2<cdots < x_n ll k$, use the Taylor expansion of $logleft(1+frac x_ikright)$. Then continuing with Taylor series
                      $$y_n=e^log(y_n)implies
                      y_n=k+fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$
                      making
                      $$ sqrt[n] prod_i=1^n(x_i+k) - k =fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$



                      For example, using $n=10$, $k=1000$ and $x_i=p_i$, the approximation would give $frac2572671200000approx 12.8634$ while the exact calculation would lead to $approx 12.8639$






                      share|cite|improve this answer


















                      • 1




                        @GustavoAle. For sure ! I just wanted to see what would be the asymptotics. Cheers.
                        – Claude Leibovici
                        24 mins ago










                      • At this point the equality is proven by applying the limit itself,$$lim_ktoinftysqrt[n] prod_i=1^n(x_i+k) - k = lim_ktoinftyfracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$ where $$lim_ktoinftyfracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k + Oleft(frac1k^2right) to 0$$ remaining only $$fracsum_i=1^n x_in$$
                        – Gustavo Ale
                        23 mins ago












                      up vote
                      2
                      down vote










                      up vote
                      2
                      down vote









                      You could even get an interesting asymptotics considering $$y_n=sqrt[n] prod_i=1^n(x_i+k)$$ $$ log(y_n)=frac 1n sum_i=1^nlogleft((x_i+k)right)=frac 1n sum_i=1^nleft(log(k)+logleft(1+frac x_ikright)right)$$ Assuming that $x_1<x_2<cdots < x_n ll k$, use the Taylor expansion of $logleft(1+frac x_ikright)$. Then continuing with Taylor series
                      $$y_n=e^log(y_n)implies
                      y_n=k+fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$
                      making
                      $$ sqrt[n] prod_i=1^n(x_i+k) - k =fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$



                      For example, using $n=10$, $k=1000$ and $x_i=p_i$, the approximation would give $frac2572671200000approx 12.8634$ while the exact calculation would lead to $approx 12.8639$






                      share|cite|improve this answer














                      You could even get an interesting asymptotics considering $$y_n=sqrt[n] prod_i=1^n(x_i+k)$$ $$ log(y_n)=frac 1n sum_i=1^nlogleft((x_i+k)right)=frac 1n sum_i=1^nleft(log(k)+logleft(1+frac x_ikright)right)$$ Assuming that $x_1<x_2<cdots < x_n ll k$, use the Taylor expansion of $logleft(1+frac x_ikright)$. Then continuing with Taylor series
                      $$y_n=e^log(y_n)implies
                      y_n=k+fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$
                      making
                      $$ sqrt[n] prod_i=1^n(x_i+k) - k =fracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$



                      For example, using $n=10$, $k=1000$ and $x_i=p_i$, the approximation would give $frac2572671200000approx 12.8634$ while the exact calculation would lead to $approx 12.8639$







                      share|cite|improve this answer














                      share|cite|improve this answer



                      share|cite|improve this answer








                      edited 40 mins ago

























                      answered 51 mins ago









                      Claude Leibovici

                      115k1155130




                      115k1155130







                      • 1




                        @GustavoAle. For sure ! I just wanted to see what would be the asymptotics. Cheers.
                        – Claude Leibovici
                        24 mins ago










                      • At this point the equality is proven by applying the limit itself,$$lim_ktoinftysqrt[n] prod_i=1^n(x_i+k) - k = lim_ktoinftyfracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$ where $$lim_ktoinftyfracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k + Oleft(frac1k^2right) to 0$$ remaining only $$fracsum_i=1^n x_in$$
                        – Gustavo Ale
                        23 mins ago












                      • 1




                        @GustavoAle. For sure ! I just wanted to see what would be the asymptotics. Cheers.
                        – Claude Leibovici
                        24 mins ago










                      • At this point the equality is proven by applying the limit itself,$$lim_ktoinftysqrt[n] prod_i=1^n(x_i+k) - k = lim_ktoinftyfracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$ where $$lim_ktoinftyfracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k + Oleft(frac1k^2right) to 0$$ remaining only $$fracsum_i=1^n x_in$$
                        – Gustavo Ale
                        23 mins ago







                      1




                      1




                      @GustavoAle. For sure ! I just wanted to see what would be the asymptotics. Cheers.
                      – Claude Leibovici
                      24 mins ago




                      @GustavoAle. For sure ! I just wanted to see what would be the asymptotics. Cheers.
                      – Claude Leibovici
                      24 mins ago












                      At this point the equality is proven by applying the limit itself,$$lim_ktoinftysqrt[n] prod_i=1^n(x_i+k) - k = lim_ktoinftyfracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$ where $$lim_ktoinftyfracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k + Oleft(frac1k^2right) to 0$$ remaining only $$fracsum_i=1^n x_in$$
                      – Gustavo Ale
                      23 mins ago




                      At this point the equality is proven by applying the limit itself,$$lim_ktoinftysqrt[n] prod_i=1^n(x_i+k) - k = lim_ktoinftyfracsum_i=1^n x_in+fracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k+Oleft(frac1k^2right)$$ where $$lim_ktoinftyfracleft(sum_i=1^n x_iright)^2-nsum_i=1^n x^2_i2n^2 k + Oleft(frac1k^2right) to 0$$ remaining only $$fracsum_i=1^n x_in$$
                      – Gustavo Ale
                      23 mins ago










                      up vote
                      1
                      down vote













                      Your expression under limit can be written as $k(a-b) $ and further $a-b=(a^n-b^n) / c$ where $a, b$ tend to $1$ and $c$ tends to $n$ as $ktoinfty$. And $$a^n-b^n=prod_i=1^nleft(1+fracx_ikright)-1$$ I hope you can continue it from here.






                      share|cite|improve this answer
























                        up vote
                        1
                        down vote













                        Your expression under limit can be written as $k(a-b) $ and further $a-b=(a^n-b^n) / c$ where $a, b$ tend to $1$ and $c$ tends to $n$ as $ktoinfty$. And $$a^n-b^n=prod_i=1^nleft(1+fracx_ikright)-1$$ I hope you can continue it from here.






                        share|cite|improve this answer






















                          up vote
                          1
                          down vote










                          up vote
                          1
                          down vote









                          Your expression under limit can be written as $k(a-b) $ and further $a-b=(a^n-b^n) / c$ where $a, b$ tend to $1$ and $c$ tends to $n$ as $ktoinfty$. And $$a^n-b^n=prod_i=1^nleft(1+fracx_ikright)-1$$ I hope you can continue it from here.






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                          Your expression under limit can be written as $k(a-b) $ and further $a-b=(a^n-b^n) / c$ where $a, b$ tend to $1$ and $c$ tends to $n$ as $ktoinfty$. And $$a^n-b^n=prod_i=1^nleft(1+fracx_ikright)-1$$ I hope you can continue it from here.







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                          answered 3 hours ago









                          Paramanand Singh

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