Maximize $x_1^3+x_2^3+cdots + x_n^3$











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This is from a Brazilian math contest for college students (OBMU):



Given a positive integer $n$, find the maximum value of



$$x_1^3+x_2^3+ cdots + x_n^3$$



where $x_j$ is a real number for all $j in {1,2,cdots, n}$ such that $x_1 + x_2 + cdots + x_n = 0$ and $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ .










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  • 2




    What have you tried?
    – Frpzzd
    Nov 19 at 23:49










  • What have you tried?
    – stuart stevenson
    Nov 19 at 23:49






  • 1




    @stuartstevenson Jinx. XD $space$
    – Frpzzd
    Nov 19 at 23:49










  • Nothing much. I just realized that the maximum value is less than 1, which is obvious.
    – Rafael Deiga
    Nov 19 at 23:51






  • 1




    Are you familiar with en.wikipedia.org/wiki/Lagrange_multiplier?
    – Federico
    Nov 20 at 0:37















up vote
5
down vote

favorite
4












This is from a Brazilian math contest for college students (OBMU):



Given a positive integer $n$, find the maximum value of



$$x_1^3+x_2^3+ cdots + x_n^3$$



where $x_j$ is a real number for all $j in {1,2,cdots, n}$ such that $x_1 + x_2 + cdots + x_n = 0$ and $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ .










share|cite|improve this question


















  • 2




    What have you tried?
    – Frpzzd
    Nov 19 at 23:49










  • What have you tried?
    – stuart stevenson
    Nov 19 at 23:49






  • 1




    @stuartstevenson Jinx. XD $space$
    – Frpzzd
    Nov 19 at 23:49










  • Nothing much. I just realized that the maximum value is less than 1, which is obvious.
    – Rafael Deiga
    Nov 19 at 23:51






  • 1




    Are you familiar with en.wikipedia.org/wiki/Lagrange_multiplier?
    – Federico
    Nov 20 at 0:37













up vote
5
down vote

favorite
4









up vote
5
down vote

favorite
4






4





This is from a Brazilian math contest for college students (OBMU):



Given a positive integer $n$, find the maximum value of



$$x_1^3+x_2^3+ cdots + x_n^3$$



where $x_j$ is a real number for all $j in {1,2,cdots, n}$ such that $x_1 + x_2 + cdots + x_n = 0$ and $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ .










share|cite|improve this question













This is from a Brazilian math contest for college students (OBMU):



Given a positive integer $n$, find the maximum value of



$$x_1^3+x_2^3+ cdots + x_n^3$$



where $x_j$ is a real number for all $j in {1,2,cdots, n}$ such that $x_1 + x_2 + cdots + x_n = 0$ and $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ .







inequality optimization






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asked Nov 19 at 23:47









Rafael Deiga

657311




657311








  • 2




    What have you tried?
    – Frpzzd
    Nov 19 at 23:49










  • What have you tried?
    – stuart stevenson
    Nov 19 at 23:49






  • 1




    @stuartstevenson Jinx. XD $space$
    – Frpzzd
    Nov 19 at 23:49










  • Nothing much. I just realized that the maximum value is less than 1, which is obvious.
    – Rafael Deiga
    Nov 19 at 23:51






  • 1




    Are you familiar with en.wikipedia.org/wiki/Lagrange_multiplier?
    – Federico
    Nov 20 at 0:37














  • 2




    What have you tried?
    – Frpzzd
    Nov 19 at 23:49










  • What have you tried?
    – stuart stevenson
    Nov 19 at 23:49






  • 1




    @stuartstevenson Jinx. XD $space$
    – Frpzzd
    Nov 19 at 23:49










  • Nothing much. I just realized that the maximum value is less than 1, which is obvious.
    – Rafael Deiga
    Nov 19 at 23:51






  • 1




    Are you familiar with en.wikipedia.org/wiki/Lagrange_multiplier?
    – Federico
    Nov 20 at 0:37








2




2




What have you tried?
– Frpzzd
Nov 19 at 23:49




What have you tried?
– Frpzzd
Nov 19 at 23:49












What have you tried?
– stuart stevenson
Nov 19 at 23:49




What have you tried?
– stuart stevenson
Nov 19 at 23:49




1




1




@stuartstevenson Jinx. XD $space$
– Frpzzd
Nov 19 at 23:49




@stuartstevenson Jinx. XD $space$
– Frpzzd
Nov 19 at 23:49












Nothing much. I just realized that the maximum value is less than 1, which is obvious.
– Rafael Deiga
Nov 19 at 23:51




Nothing much. I just realized that the maximum value is less than 1, which is obvious.
– Rafael Deiga
Nov 19 at 23:51




1




1




Are you familiar with en.wikipedia.org/wiki/Lagrange_multiplier?
– Federico
Nov 20 at 0:37




Are you familiar with en.wikipedia.org/wiki/Lagrange_multiplier?
– Federico
Nov 20 at 0:37










3 Answers
3






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4
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One could indeed use Lagrange multipliers, but I thought I'd try to get geometric intuition in three dimensions.



Constraint sphere



This shows the constraint sphere $x_1^2 + x_2^2 + x_3^2=1$ and the constraint plane $x_1 + x_2 + x_3 = 0$, and the locus of their intersection, a (black) ring. The plane is perpendicular to the normalized vector ${bf a} = (1/sqrt{3}, 1/sqrt{3}, 1/sqrt{3})$. One (normalized) vector perpendicular to ${bf a}$ is ${bf b} = left( frac{1}{sqrt{6}},frac{1}{sqrt{6}},-frac{2}{sqrt{6}} right)$. Another (normalized) vector perpendicular to both is ${bf c} = left( -frac{1}{sqrt{2}},frac{1}{sqrt{2}},0right)$, determined by ${bf c} = {bf a} times {bf b}$.



Thus any potential solution point can be described as $(x_1, x_2, x_3) = cos (theta) {bf b} + sin (theta) {bf c}$.



Direct substitution, cubing the components and simplification yields $x_1^3 + x_2^3 + x_3^3 = -frac{cos (3 theta )}{sqrt{6}}$.



It is a simple matter to maximize this function of a single variable $theta$ and find that the solution is $1/sqrt{6}$.



enter image description here



Not surprisingly there are three equivalent solutions, corresponding to the permutation of the three variables.



As a check, I find the solution vector with $theta = 1$ (from the graph) to be ${bf s} = (-0.374432, 0.815587, -0.441155)$ indeed obeys the constraints and leads to the criterion sum-of-cubes to be $0.404163$, as visible on the graph.






share|cite|improve this answer























  • For $n=4$ taking $x_1=x_2=x_3=-frac1{sqrt{12}}$ and $x_4=frac3{sqrt{12}}$ gives $x_1^3+x_2^3+x_3^3+x_4^3=1/sqrt3$ perhaps this is helpful.
    – kingW3
    Nov 20 at 2:03










  • The maximum should be an increasing sequence with $n$: let $f_k(vec{x})=sum x_i^k$. If for $vec{a}=(a_1,a_2,ldots ,a_n)$, $f_1(vec{a})=0$ and $f_2(vec{a})=1$, then for $vec{b}=(a_1,a_2,ldots , a_n, 0)$, we also have $f_1(vec{b})=0$, $f_2(vec{b})=1$, and $f_3(vec{a})=f_3(vec{b})$.
    – Jacob
    Nov 20 at 2:42




















up vote
2
down vote













Hint: Try to use the technique of Fourier Analysis. We can reasonably guess that the maximum occurs when $x_i = -frac{1}{sqrt{n(n-1)}}, forall i<n$ and $x_n = sqrt{1-frac{1}{n}}.$



View $x_{cdot}$ as a function defined on the group $mathbf{Z}/nmathbf{Z}$. Let $zeta = exp(frac{2pi i}{n})$ be $n$-th root of unity and define its $r$-th Fourier coefficient as $s^{}(r) = frac{1}{n}sum_{k=1}^{n}x_kzeta^{-rk}.$ We note that
$$x_j = sum_{r=0}^{n-1}s(r)zeta^{rj}, quad forall j=1,ldots,n,$$ and
$$ sum_{1leq jleq n} |x_j|^2 = nsum_{1leq rleq n}|s(r)|^2,
$$
since $frac{1}{n}sum_{1leq rleq n} zeta^{rm} =1_{{m=0}}$. Express the constraints in the language of $s(r)$ (including $x$ is real-valued.) Then we get
$$s(r) =overline{s(n-r)},; s(0) = 0,; sum_{1leq rleq n}|s(r)|^2 = frac{1}{n}. $$
What is left is to express $Q = x_1^3 + cdots + x_n^3$ as a function involving $s(r)$'s. From the first identity above, we have
$$Q = nsum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r'). $$ Note that the support is
$$R = {(r,r');| ;1leq r,r'leq n-1, rneq r'}.$$ By applying Cauchy-Schwarz, we have
$$begin{eqnarray}|sum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r')|^2&leq& sum_{(r,r')in R}|s(r)|^2|s(r-r')|^2sum_{(r,r')in R}|s(r')|^2 \
&=&sum_{1 leq rleq n-1}|s(r)|^2left(frac{1}{n} - |s(r)|^2right)cdotsum_{1 leq rleq n-1}frac{1}{n} - |s(r)|^2 \
&=&left(frac{1}{n^2} - sum_{1 leq rleq n-1} |s(r)|^4right)frac{n-2}{n}\
&leq& frac{(n-2)^2}{n^3(n-1)},
end{eqnarray}$$
since $frac{1}{n^2} leq (n-1)sum_{1 leq rleq n-1} |s(r)|^4$.
Thus, we have
$$Q leq frac{n-2}{sqrt{n}sqrt{n-1}},$$ as desired.






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    I come up with a solution using Lagrange Multipliers (one of the comments and one of answers suggests to use this). First, we need to realize that the domain



    $$ S ={ (x_1,x_2,cdots,x_n) in {mathbb{R}}^n | x_1 + x_2 + cdots + x_n = 0 text{ and } x_1^2 + x_2^2 + cdots + x_n^2 = 1} $$



    is closed (since the functions $(x_1,x_2,cdots,x_n ) mapsto x_1 + x_2 + cdots + x_n$ and $(x_1,x_2,cdots,x_n ) mapsto x_1^2 + x_2^2 + cdots + x_n^2$ are continuous) and bounded (since $x_1^2 + x_2^2 + cdots + x_n^2 = 1$). Then S is compact.



    Define
    $$f(x_1,x_2,cdots,x_n ) = x_1^3 + x_2^3 + cdots+ x_n^3 $$
    $$g(x_1,x_2,cdots,x_n ) = x_1^2 + x_2^2 + cdots+ x_n^2 -1 $$
    $$h(x_1,x_2,cdots,x_n ) = x_1 + x_2 + cdots+ x_n $$



    Since $S$ is compact and $f$ is continuous, then $f$ reaches a maximum value in
    $S$. Using Lagrange Multiplier, we should have



    $$nabla f = lambda_1 nabla g + lambda_2 nabla h$$



    Thus,



    $$ 3(x_1^2,x_2^2,cdots,x_n^2) = 2lambda_1(x_1,x_2,cdots,x_n ) + lambda_2(1,1,cdots,1)$$



    Then,



    $$3x_i^2 = 2lambda_1 x_i+ lambda_2 text{ } forall i in {1,2,cdots,n} label{1}tag{1}$$



    If we add all the equations and use the constraints equations, we obtain $lambda_2= frac{3}{n}$. Then,



    $$3x_i^2 = 2lambda_1 x_i+ frac{3}{n} $$



    Solving for $x_i$, we obtain



    $$ x_i = frac{lambda_1 pm sqrt{lambda_1^2 + frac{9}{n}}}{3} $$



    Let $k$ be a natural number less or equal to $n$. Therefore we can suppose



    $$ x_i = frac{lambda_1 + sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {1,2,cdots,k}$$



    $$ x_i = frac{lambda_1 - sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {k+1,k+2,cdots,n}$$



    Firtly, notice that $k$ is different of $0$ and $n$, because of the constraint $x_1+ x_2 + cdots + x_n =0$. Using the constraint $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ and after some simplifications, we obtain



    $$lambda_1 left(nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} right) = 0label{2}tag{2}$$



    And using $x_1+ x_2 + cdots + x_n =0$, we get



    $$ nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} = 0 label{3}tag{3}$$



    So, we just need to satisfy ref{3}, because with that ref{2} is automatically satisfied. Then, we get



    $$lambda_1 = frac{3(n-2k)}{2sqrt{(n-k)nk}} $$



    Notice that we should consider the other solution with the other sign, but in the end we obtain the same maximum value for both cases. Multiplying ref{1} by $x_i$:



    $$ 3x_i^3 = 2lambda_1x_i^2 + frac{3x_i}{n} $$



    Adding in all i's:



    $$3f = 2lambda_1 $$



    Thus,



    $$ f(k) = frac{n-2k}{sqrt{(n-k)nk}} = frac{1}{sqrt{n}}left(sqrt{frac{n-k}{k}}-sqrt{frac{k}{n-k}}right)$$



    Remember that $k in {2,3,cdots, n-1}$. Making $x = sqrt{frac{k}{n-k}}$ and analyzing the derivative of



    $$y: (0,1) rightarrow mathbb{R}$$
    $$ x mapstofrac{1}{x}-x $$



    We see that the maximum value of $f$ is when $k=1$, that is,



    $$ frac{n-2}{sqrt{(n-1)n}} $$



    However, the Lagrange Multipliers just find the local extrema. How can I guarantee (if it's possible) that the above value is indeed the maximum value of $f$?






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      3 Answers
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      3 Answers
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      up vote
      4
      down vote













      One could indeed use Lagrange multipliers, but I thought I'd try to get geometric intuition in three dimensions.



      Constraint sphere



      This shows the constraint sphere $x_1^2 + x_2^2 + x_3^2=1$ and the constraint plane $x_1 + x_2 + x_3 = 0$, and the locus of their intersection, a (black) ring. The plane is perpendicular to the normalized vector ${bf a} = (1/sqrt{3}, 1/sqrt{3}, 1/sqrt{3})$. One (normalized) vector perpendicular to ${bf a}$ is ${bf b} = left( frac{1}{sqrt{6}},frac{1}{sqrt{6}},-frac{2}{sqrt{6}} right)$. Another (normalized) vector perpendicular to both is ${bf c} = left( -frac{1}{sqrt{2}},frac{1}{sqrt{2}},0right)$, determined by ${bf c} = {bf a} times {bf b}$.



      Thus any potential solution point can be described as $(x_1, x_2, x_3) = cos (theta) {bf b} + sin (theta) {bf c}$.



      Direct substitution, cubing the components and simplification yields $x_1^3 + x_2^3 + x_3^3 = -frac{cos (3 theta )}{sqrt{6}}$.



      It is a simple matter to maximize this function of a single variable $theta$ and find that the solution is $1/sqrt{6}$.



      enter image description here



      Not surprisingly there are three equivalent solutions, corresponding to the permutation of the three variables.



      As a check, I find the solution vector with $theta = 1$ (from the graph) to be ${bf s} = (-0.374432, 0.815587, -0.441155)$ indeed obeys the constraints and leads to the criterion sum-of-cubes to be $0.404163$, as visible on the graph.






      share|cite|improve this answer























      • For $n=4$ taking $x_1=x_2=x_3=-frac1{sqrt{12}}$ and $x_4=frac3{sqrt{12}}$ gives $x_1^3+x_2^3+x_3^3+x_4^3=1/sqrt3$ perhaps this is helpful.
        – kingW3
        Nov 20 at 2:03










      • The maximum should be an increasing sequence with $n$: let $f_k(vec{x})=sum x_i^k$. If for $vec{a}=(a_1,a_2,ldots ,a_n)$, $f_1(vec{a})=0$ and $f_2(vec{a})=1$, then for $vec{b}=(a_1,a_2,ldots , a_n, 0)$, we also have $f_1(vec{b})=0$, $f_2(vec{b})=1$, and $f_3(vec{a})=f_3(vec{b})$.
        – Jacob
        Nov 20 at 2:42

















      up vote
      4
      down vote













      One could indeed use Lagrange multipliers, but I thought I'd try to get geometric intuition in three dimensions.



      Constraint sphere



      This shows the constraint sphere $x_1^2 + x_2^2 + x_3^2=1$ and the constraint plane $x_1 + x_2 + x_3 = 0$, and the locus of their intersection, a (black) ring. The plane is perpendicular to the normalized vector ${bf a} = (1/sqrt{3}, 1/sqrt{3}, 1/sqrt{3})$. One (normalized) vector perpendicular to ${bf a}$ is ${bf b} = left( frac{1}{sqrt{6}},frac{1}{sqrt{6}},-frac{2}{sqrt{6}} right)$. Another (normalized) vector perpendicular to both is ${bf c} = left( -frac{1}{sqrt{2}},frac{1}{sqrt{2}},0right)$, determined by ${bf c} = {bf a} times {bf b}$.



      Thus any potential solution point can be described as $(x_1, x_2, x_3) = cos (theta) {bf b} + sin (theta) {bf c}$.



      Direct substitution, cubing the components and simplification yields $x_1^3 + x_2^3 + x_3^3 = -frac{cos (3 theta )}{sqrt{6}}$.



      It is a simple matter to maximize this function of a single variable $theta$ and find that the solution is $1/sqrt{6}$.



      enter image description here



      Not surprisingly there are three equivalent solutions, corresponding to the permutation of the three variables.



      As a check, I find the solution vector with $theta = 1$ (from the graph) to be ${bf s} = (-0.374432, 0.815587, -0.441155)$ indeed obeys the constraints and leads to the criterion sum-of-cubes to be $0.404163$, as visible on the graph.






      share|cite|improve this answer























      • For $n=4$ taking $x_1=x_2=x_3=-frac1{sqrt{12}}$ and $x_4=frac3{sqrt{12}}$ gives $x_1^3+x_2^3+x_3^3+x_4^3=1/sqrt3$ perhaps this is helpful.
        – kingW3
        Nov 20 at 2:03










      • The maximum should be an increasing sequence with $n$: let $f_k(vec{x})=sum x_i^k$. If for $vec{a}=(a_1,a_2,ldots ,a_n)$, $f_1(vec{a})=0$ and $f_2(vec{a})=1$, then for $vec{b}=(a_1,a_2,ldots , a_n, 0)$, we also have $f_1(vec{b})=0$, $f_2(vec{b})=1$, and $f_3(vec{a})=f_3(vec{b})$.
        – Jacob
        Nov 20 at 2:42















      up vote
      4
      down vote










      up vote
      4
      down vote









      One could indeed use Lagrange multipliers, but I thought I'd try to get geometric intuition in three dimensions.



      Constraint sphere



      This shows the constraint sphere $x_1^2 + x_2^2 + x_3^2=1$ and the constraint plane $x_1 + x_2 + x_3 = 0$, and the locus of their intersection, a (black) ring. The plane is perpendicular to the normalized vector ${bf a} = (1/sqrt{3}, 1/sqrt{3}, 1/sqrt{3})$. One (normalized) vector perpendicular to ${bf a}$ is ${bf b} = left( frac{1}{sqrt{6}},frac{1}{sqrt{6}},-frac{2}{sqrt{6}} right)$. Another (normalized) vector perpendicular to both is ${bf c} = left( -frac{1}{sqrt{2}},frac{1}{sqrt{2}},0right)$, determined by ${bf c} = {bf a} times {bf b}$.



      Thus any potential solution point can be described as $(x_1, x_2, x_3) = cos (theta) {bf b} + sin (theta) {bf c}$.



      Direct substitution, cubing the components and simplification yields $x_1^3 + x_2^3 + x_3^3 = -frac{cos (3 theta )}{sqrt{6}}$.



      It is a simple matter to maximize this function of a single variable $theta$ and find that the solution is $1/sqrt{6}$.



      enter image description here



      Not surprisingly there are three equivalent solutions, corresponding to the permutation of the three variables.



      As a check, I find the solution vector with $theta = 1$ (from the graph) to be ${bf s} = (-0.374432, 0.815587, -0.441155)$ indeed obeys the constraints and leads to the criterion sum-of-cubes to be $0.404163$, as visible on the graph.






      share|cite|improve this answer














      One could indeed use Lagrange multipliers, but I thought I'd try to get geometric intuition in three dimensions.



      Constraint sphere



      This shows the constraint sphere $x_1^2 + x_2^2 + x_3^2=1$ and the constraint plane $x_1 + x_2 + x_3 = 0$, and the locus of their intersection, a (black) ring. The plane is perpendicular to the normalized vector ${bf a} = (1/sqrt{3}, 1/sqrt{3}, 1/sqrt{3})$. One (normalized) vector perpendicular to ${bf a}$ is ${bf b} = left( frac{1}{sqrt{6}},frac{1}{sqrt{6}},-frac{2}{sqrt{6}} right)$. Another (normalized) vector perpendicular to both is ${bf c} = left( -frac{1}{sqrt{2}},frac{1}{sqrt{2}},0right)$, determined by ${bf c} = {bf a} times {bf b}$.



      Thus any potential solution point can be described as $(x_1, x_2, x_3) = cos (theta) {bf b} + sin (theta) {bf c}$.



      Direct substitution, cubing the components and simplification yields $x_1^3 + x_2^3 + x_3^3 = -frac{cos (3 theta )}{sqrt{6}}$.



      It is a simple matter to maximize this function of a single variable $theta$ and find that the solution is $1/sqrt{6}$.



      enter image description here



      Not surprisingly there are three equivalent solutions, corresponding to the permutation of the three variables.



      As a check, I find the solution vector with $theta = 1$ (from the graph) to be ${bf s} = (-0.374432, 0.815587, -0.441155)$ indeed obeys the constraints and leads to the criterion sum-of-cubes to be $0.404163$, as visible on the graph.







      share|cite|improve this answer














      share|cite|improve this answer



      share|cite|improve this answer








      edited Nov 20 at 2:45

























      answered Nov 20 at 0:53









      David G. Stork

      9,22221232




      9,22221232












      • For $n=4$ taking $x_1=x_2=x_3=-frac1{sqrt{12}}$ and $x_4=frac3{sqrt{12}}$ gives $x_1^3+x_2^3+x_3^3+x_4^3=1/sqrt3$ perhaps this is helpful.
        – kingW3
        Nov 20 at 2:03










      • The maximum should be an increasing sequence with $n$: let $f_k(vec{x})=sum x_i^k$. If for $vec{a}=(a_1,a_2,ldots ,a_n)$, $f_1(vec{a})=0$ and $f_2(vec{a})=1$, then for $vec{b}=(a_1,a_2,ldots , a_n, 0)$, we also have $f_1(vec{b})=0$, $f_2(vec{b})=1$, and $f_3(vec{a})=f_3(vec{b})$.
        – Jacob
        Nov 20 at 2:42




















      • For $n=4$ taking $x_1=x_2=x_3=-frac1{sqrt{12}}$ and $x_4=frac3{sqrt{12}}$ gives $x_1^3+x_2^3+x_3^3+x_4^3=1/sqrt3$ perhaps this is helpful.
        – kingW3
        Nov 20 at 2:03










      • The maximum should be an increasing sequence with $n$: let $f_k(vec{x})=sum x_i^k$. If for $vec{a}=(a_1,a_2,ldots ,a_n)$, $f_1(vec{a})=0$ and $f_2(vec{a})=1$, then for $vec{b}=(a_1,a_2,ldots , a_n, 0)$, we also have $f_1(vec{b})=0$, $f_2(vec{b})=1$, and $f_3(vec{a})=f_3(vec{b})$.
        – Jacob
        Nov 20 at 2:42


















      For $n=4$ taking $x_1=x_2=x_3=-frac1{sqrt{12}}$ and $x_4=frac3{sqrt{12}}$ gives $x_1^3+x_2^3+x_3^3+x_4^3=1/sqrt3$ perhaps this is helpful.
      – kingW3
      Nov 20 at 2:03




      For $n=4$ taking $x_1=x_2=x_3=-frac1{sqrt{12}}$ and $x_4=frac3{sqrt{12}}$ gives $x_1^3+x_2^3+x_3^3+x_4^3=1/sqrt3$ perhaps this is helpful.
      – kingW3
      Nov 20 at 2:03












      The maximum should be an increasing sequence with $n$: let $f_k(vec{x})=sum x_i^k$. If for $vec{a}=(a_1,a_2,ldots ,a_n)$, $f_1(vec{a})=0$ and $f_2(vec{a})=1$, then for $vec{b}=(a_1,a_2,ldots , a_n, 0)$, we also have $f_1(vec{b})=0$, $f_2(vec{b})=1$, and $f_3(vec{a})=f_3(vec{b})$.
      – Jacob
      Nov 20 at 2:42






      The maximum should be an increasing sequence with $n$: let $f_k(vec{x})=sum x_i^k$. If for $vec{a}=(a_1,a_2,ldots ,a_n)$, $f_1(vec{a})=0$ and $f_2(vec{a})=1$, then for $vec{b}=(a_1,a_2,ldots , a_n, 0)$, we also have $f_1(vec{b})=0$, $f_2(vec{b})=1$, and $f_3(vec{a})=f_3(vec{b})$.
      – Jacob
      Nov 20 at 2:42












      up vote
      2
      down vote













      Hint: Try to use the technique of Fourier Analysis. We can reasonably guess that the maximum occurs when $x_i = -frac{1}{sqrt{n(n-1)}}, forall i<n$ and $x_n = sqrt{1-frac{1}{n}}.$



      View $x_{cdot}$ as a function defined on the group $mathbf{Z}/nmathbf{Z}$. Let $zeta = exp(frac{2pi i}{n})$ be $n$-th root of unity and define its $r$-th Fourier coefficient as $s^{}(r) = frac{1}{n}sum_{k=1}^{n}x_kzeta^{-rk}.$ We note that
      $$x_j = sum_{r=0}^{n-1}s(r)zeta^{rj}, quad forall j=1,ldots,n,$$ and
      $$ sum_{1leq jleq n} |x_j|^2 = nsum_{1leq rleq n}|s(r)|^2,
      $$
      since $frac{1}{n}sum_{1leq rleq n} zeta^{rm} =1_{{m=0}}$. Express the constraints in the language of $s(r)$ (including $x$ is real-valued.) Then we get
      $$s(r) =overline{s(n-r)},; s(0) = 0,; sum_{1leq rleq n}|s(r)|^2 = frac{1}{n}. $$
      What is left is to express $Q = x_1^3 + cdots + x_n^3$ as a function involving $s(r)$'s. From the first identity above, we have
      $$Q = nsum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r'). $$ Note that the support is
      $$R = {(r,r');| ;1leq r,r'leq n-1, rneq r'}.$$ By applying Cauchy-Schwarz, we have
      $$begin{eqnarray}|sum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r')|^2&leq& sum_{(r,r')in R}|s(r)|^2|s(r-r')|^2sum_{(r,r')in R}|s(r')|^2 \
      &=&sum_{1 leq rleq n-1}|s(r)|^2left(frac{1}{n} - |s(r)|^2right)cdotsum_{1 leq rleq n-1}frac{1}{n} - |s(r)|^2 \
      &=&left(frac{1}{n^2} - sum_{1 leq rleq n-1} |s(r)|^4right)frac{n-2}{n}\
      &leq& frac{(n-2)^2}{n^3(n-1)},
      end{eqnarray}$$
      since $frac{1}{n^2} leq (n-1)sum_{1 leq rleq n-1} |s(r)|^4$.
      Thus, we have
      $$Q leq frac{n-2}{sqrt{n}sqrt{n-1}},$$ as desired.






      share|cite|improve this answer



























        up vote
        2
        down vote













        Hint: Try to use the technique of Fourier Analysis. We can reasonably guess that the maximum occurs when $x_i = -frac{1}{sqrt{n(n-1)}}, forall i<n$ and $x_n = sqrt{1-frac{1}{n}}.$



        View $x_{cdot}$ as a function defined on the group $mathbf{Z}/nmathbf{Z}$. Let $zeta = exp(frac{2pi i}{n})$ be $n$-th root of unity and define its $r$-th Fourier coefficient as $s^{}(r) = frac{1}{n}sum_{k=1}^{n}x_kzeta^{-rk}.$ We note that
        $$x_j = sum_{r=0}^{n-1}s(r)zeta^{rj}, quad forall j=1,ldots,n,$$ and
        $$ sum_{1leq jleq n} |x_j|^2 = nsum_{1leq rleq n}|s(r)|^2,
        $$
        since $frac{1}{n}sum_{1leq rleq n} zeta^{rm} =1_{{m=0}}$. Express the constraints in the language of $s(r)$ (including $x$ is real-valued.) Then we get
        $$s(r) =overline{s(n-r)},; s(0) = 0,; sum_{1leq rleq n}|s(r)|^2 = frac{1}{n}. $$
        What is left is to express $Q = x_1^3 + cdots + x_n^3$ as a function involving $s(r)$'s. From the first identity above, we have
        $$Q = nsum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r'). $$ Note that the support is
        $$R = {(r,r');| ;1leq r,r'leq n-1, rneq r'}.$$ By applying Cauchy-Schwarz, we have
        $$begin{eqnarray}|sum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r')|^2&leq& sum_{(r,r')in R}|s(r)|^2|s(r-r')|^2sum_{(r,r')in R}|s(r')|^2 \
        &=&sum_{1 leq rleq n-1}|s(r)|^2left(frac{1}{n} - |s(r)|^2right)cdotsum_{1 leq rleq n-1}frac{1}{n} - |s(r)|^2 \
        &=&left(frac{1}{n^2} - sum_{1 leq rleq n-1} |s(r)|^4right)frac{n-2}{n}\
        &leq& frac{(n-2)^2}{n^3(n-1)},
        end{eqnarray}$$
        since $frac{1}{n^2} leq (n-1)sum_{1 leq rleq n-1} |s(r)|^4$.
        Thus, we have
        $$Q leq frac{n-2}{sqrt{n}sqrt{n-1}},$$ as desired.






        share|cite|improve this answer

























          up vote
          2
          down vote










          up vote
          2
          down vote









          Hint: Try to use the technique of Fourier Analysis. We can reasonably guess that the maximum occurs when $x_i = -frac{1}{sqrt{n(n-1)}}, forall i<n$ and $x_n = sqrt{1-frac{1}{n}}.$



          View $x_{cdot}$ as a function defined on the group $mathbf{Z}/nmathbf{Z}$. Let $zeta = exp(frac{2pi i}{n})$ be $n$-th root of unity and define its $r$-th Fourier coefficient as $s^{}(r) = frac{1}{n}sum_{k=1}^{n}x_kzeta^{-rk}.$ We note that
          $$x_j = sum_{r=0}^{n-1}s(r)zeta^{rj}, quad forall j=1,ldots,n,$$ and
          $$ sum_{1leq jleq n} |x_j|^2 = nsum_{1leq rleq n}|s(r)|^2,
          $$
          since $frac{1}{n}sum_{1leq rleq n} zeta^{rm} =1_{{m=0}}$. Express the constraints in the language of $s(r)$ (including $x$ is real-valued.) Then we get
          $$s(r) =overline{s(n-r)},; s(0) = 0,; sum_{1leq rleq n}|s(r)|^2 = frac{1}{n}. $$
          What is left is to express $Q = x_1^3 + cdots + x_n^3$ as a function involving $s(r)$'s. From the first identity above, we have
          $$Q = nsum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r'). $$ Note that the support is
          $$R = {(r,r');| ;1leq r,r'leq n-1, rneq r'}.$$ By applying Cauchy-Schwarz, we have
          $$begin{eqnarray}|sum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r')|^2&leq& sum_{(r,r')in R}|s(r)|^2|s(r-r')|^2sum_{(r,r')in R}|s(r')|^2 \
          &=&sum_{1 leq rleq n-1}|s(r)|^2left(frac{1}{n} - |s(r)|^2right)cdotsum_{1 leq rleq n-1}frac{1}{n} - |s(r)|^2 \
          &=&left(frac{1}{n^2} - sum_{1 leq rleq n-1} |s(r)|^4right)frac{n-2}{n}\
          &leq& frac{(n-2)^2}{n^3(n-1)},
          end{eqnarray}$$
          since $frac{1}{n^2} leq (n-1)sum_{1 leq rleq n-1} |s(r)|^4$.
          Thus, we have
          $$Q leq frac{n-2}{sqrt{n}sqrt{n-1}},$$ as desired.






          share|cite|improve this answer














          Hint: Try to use the technique of Fourier Analysis. We can reasonably guess that the maximum occurs when $x_i = -frac{1}{sqrt{n(n-1)}}, forall i<n$ and $x_n = sqrt{1-frac{1}{n}}.$



          View $x_{cdot}$ as a function defined on the group $mathbf{Z}/nmathbf{Z}$. Let $zeta = exp(frac{2pi i}{n})$ be $n$-th root of unity and define its $r$-th Fourier coefficient as $s^{}(r) = frac{1}{n}sum_{k=1}^{n}x_kzeta^{-rk}.$ We note that
          $$x_j = sum_{r=0}^{n-1}s(r)zeta^{rj}, quad forall j=1,ldots,n,$$ and
          $$ sum_{1leq jleq n} |x_j|^2 = nsum_{1leq rleq n}|s(r)|^2,
          $$
          since $frac{1}{n}sum_{1leq rleq n} zeta^{rm} =1_{{m=0}}$. Express the constraints in the language of $s(r)$ (including $x$ is real-valued.) Then we get
          $$s(r) =overline{s(n-r)},; s(0) = 0,; sum_{1leq rleq n}|s(r)|^2 = frac{1}{n}. $$
          What is left is to express $Q = x_1^3 + cdots + x_n^3$ as a function involving $s(r)$'s. From the first identity above, we have
          $$Q = nsum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r'). $$ Note that the support is
          $$R = {(r,r');| ;1leq r,r'leq n-1, rneq r'}.$$ By applying Cauchy-Schwarz, we have
          $$begin{eqnarray}|sum_{1leq r,r'leq n}s(r)overline{s(r')}s(r-r')|^2&leq& sum_{(r,r')in R}|s(r)|^2|s(r-r')|^2sum_{(r,r')in R}|s(r')|^2 \
          &=&sum_{1 leq rleq n-1}|s(r)|^2left(frac{1}{n} - |s(r)|^2right)cdotsum_{1 leq rleq n-1}frac{1}{n} - |s(r)|^2 \
          &=&left(frac{1}{n^2} - sum_{1 leq rleq n-1} |s(r)|^4right)frac{n-2}{n}\
          &leq& frac{(n-2)^2}{n^3(n-1)},
          end{eqnarray}$$
          since $frac{1}{n^2} leq (n-1)sum_{1 leq rleq n-1} |s(r)|^4$.
          Thus, we have
          $$Q leq frac{n-2}{sqrt{n}sqrt{n-1}},$$ as desired.







          share|cite|improve this answer














          share|cite|improve this answer



          share|cite|improve this answer








          edited Nov 20 at 4:11

























          answered Nov 20 at 4:05









          Song

          73510




          73510






















              up vote
              1
              down vote













              I come up with a solution using Lagrange Multipliers (one of the comments and one of answers suggests to use this). First, we need to realize that the domain



              $$ S ={ (x_1,x_2,cdots,x_n) in {mathbb{R}}^n | x_1 + x_2 + cdots + x_n = 0 text{ and } x_1^2 + x_2^2 + cdots + x_n^2 = 1} $$



              is closed (since the functions $(x_1,x_2,cdots,x_n ) mapsto x_1 + x_2 + cdots + x_n$ and $(x_1,x_2,cdots,x_n ) mapsto x_1^2 + x_2^2 + cdots + x_n^2$ are continuous) and bounded (since $x_1^2 + x_2^2 + cdots + x_n^2 = 1$). Then S is compact.



              Define
              $$f(x_1,x_2,cdots,x_n ) = x_1^3 + x_2^3 + cdots+ x_n^3 $$
              $$g(x_1,x_2,cdots,x_n ) = x_1^2 + x_2^2 + cdots+ x_n^2 -1 $$
              $$h(x_1,x_2,cdots,x_n ) = x_1 + x_2 + cdots+ x_n $$



              Since $S$ is compact and $f$ is continuous, then $f$ reaches a maximum value in
              $S$. Using Lagrange Multiplier, we should have



              $$nabla f = lambda_1 nabla g + lambda_2 nabla h$$



              Thus,



              $$ 3(x_1^2,x_2^2,cdots,x_n^2) = 2lambda_1(x_1,x_2,cdots,x_n ) + lambda_2(1,1,cdots,1)$$



              Then,



              $$3x_i^2 = 2lambda_1 x_i+ lambda_2 text{ } forall i in {1,2,cdots,n} label{1}tag{1}$$



              If we add all the equations and use the constraints equations, we obtain $lambda_2= frac{3}{n}$. Then,



              $$3x_i^2 = 2lambda_1 x_i+ frac{3}{n} $$



              Solving for $x_i$, we obtain



              $$ x_i = frac{lambda_1 pm sqrt{lambda_1^2 + frac{9}{n}}}{3} $$



              Let $k$ be a natural number less or equal to $n$. Therefore we can suppose



              $$ x_i = frac{lambda_1 + sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {1,2,cdots,k}$$



              $$ x_i = frac{lambda_1 - sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {k+1,k+2,cdots,n}$$



              Firtly, notice that $k$ is different of $0$ and $n$, because of the constraint $x_1+ x_2 + cdots + x_n =0$. Using the constraint $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ and after some simplifications, we obtain



              $$lambda_1 left(nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} right) = 0label{2}tag{2}$$



              And using $x_1+ x_2 + cdots + x_n =0$, we get



              $$ nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} = 0 label{3}tag{3}$$



              So, we just need to satisfy ref{3}, because with that ref{2} is automatically satisfied. Then, we get



              $$lambda_1 = frac{3(n-2k)}{2sqrt{(n-k)nk}} $$



              Notice that we should consider the other solution with the other sign, but in the end we obtain the same maximum value for both cases. Multiplying ref{1} by $x_i$:



              $$ 3x_i^3 = 2lambda_1x_i^2 + frac{3x_i}{n} $$



              Adding in all i's:



              $$3f = 2lambda_1 $$



              Thus,



              $$ f(k) = frac{n-2k}{sqrt{(n-k)nk}} = frac{1}{sqrt{n}}left(sqrt{frac{n-k}{k}}-sqrt{frac{k}{n-k}}right)$$



              Remember that $k in {2,3,cdots, n-1}$. Making $x = sqrt{frac{k}{n-k}}$ and analyzing the derivative of



              $$y: (0,1) rightarrow mathbb{R}$$
              $$ x mapstofrac{1}{x}-x $$



              We see that the maximum value of $f$ is when $k=1$, that is,



              $$ frac{n-2}{sqrt{(n-1)n}} $$



              However, the Lagrange Multipliers just find the local extrema. How can I guarantee (if it's possible) that the above value is indeed the maximum value of $f$?






              share|cite|improve this answer



























                up vote
                1
                down vote













                I come up with a solution using Lagrange Multipliers (one of the comments and one of answers suggests to use this). First, we need to realize that the domain



                $$ S ={ (x_1,x_2,cdots,x_n) in {mathbb{R}}^n | x_1 + x_2 + cdots + x_n = 0 text{ and } x_1^2 + x_2^2 + cdots + x_n^2 = 1} $$



                is closed (since the functions $(x_1,x_2,cdots,x_n ) mapsto x_1 + x_2 + cdots + x_n$ and $(x_1,x_2,cdots,x_n ) mapsto x_1^2 + x_2^2 + cdots + x_n^2$ are continuous) and bounded (since $x_1^2 + x_2^2 + cdots + x_n^2 = 1$). Then S is compact.



                Define
                $$f(x_1,x_2,cdots,x_n ) = x_1^3 + x_2^3 + cdots+ x_n^3 $$
                $$g(x_1,x_2,cdots,x_n ) = x_1^2 + x_2^2 + cdots+ x_n^2 -1 $$
                $$h(x_1,x_2,cdots,x_n ) = x_1 + x_2 + cdots+ x_n $$



                Since $S$ is compact and $f$ is continuous, then $f$ reaches a maximum value in
                $S$. Using Lagrange Multiplier, we should have



                $$nabla f = lambda_1 nabla g + lambda_2 nabla h$$



                Thus,



                $$ 3(x_1^2,x_2^2,cdots,x_n^2) = 2lambda_1(x_1,x_2,cdots,x_n ) + lambda_2(1,1,cdots,1)$$



                Then,



                $$3x_i^2 = 2lambda_1 x_i+ lambda_2 text{ } forall i in {1,2,cdots,n} label{1}tag{1}$$



                If we add all the equations and use the constraints equations, we obtain $lambda_2= frac{3}{n}$. Then,



                $$3x_i^2 = 2lambda_1 x_i+ frac{3}{n} $$



                Solving for $x_i$, we obtain



                $$ x_i = frac{lambda_1 pm sqrt{lambda_1^2 + frac{9}{n}}}{3} $$



                Let $k$ be a natural number less or equal to $n$. Therefore we can suppose



                $$ x_i = frac{lambda_1 + sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {1,2,cdots,k}$$



                $$ x_i = frac{lambda_1 - sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {k+1,k+2,cdots,n}$$



                Firtly, notice that $k$ is different of $0$ and $n$, because of the constraint $x_1+ x_2 + cdots + x_n =0$. Using the constraint $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ and after some simplifications, we obtain



                $$lambda_1 left(nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} right) = 0label{2}tag{2}$$



                And using $x_1+ x_2 + cdots + x_n =0$, we get



                $$ nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} = 0 label{3}tag{3}$$



                So, we just need to satisfy ref{3}, because with that ref{2} is automatically satisfied. Then, we get



                $$lambda_1 = frac{3(n-2k)}{2sqrt{(n-k)nk}} $$



                Notice that we should consider the other solution with the other sign, but in the end we obtain the same maximum value for both cases. Multiplying ref{1} by $x_i$:



                $$ 3x_i^3 = 2lambda_1x_i^2 + frac{3x_i}{n} $$



                Adding in all i's:



                $$3f = 2lambda_1 $$



                Thus,



                $$ f(k) = frac{n-2k}{sqrt{(n-k)nk}} = frac{1}{sqrt{n}}left(sqrt{frac{n-k}{k}}-sqrt{frac{k}{n-k}}right)$$



                Remember that $k in {2,3,cdots, n-1}$. Making $x = sqrt{frac{k}{n-k}}$ and analyzing the derivative of



                $$y: (0,1) rightarrow mathbb{R}$$
                $$ x mapstofrac{1}{x}-x $$



                We see that the maximum value of $f$ is when $k=1$, that is,



                $$ frac{n-2}{sqrt{(n-1)n}} $$



                However, the Lagrange Multipliers just find the local extrema. How can I guarantee (if it's possible) that the above value is indeed the maximum value of $f$?






                share|cite|improve this answer

























                  up vote
                  1
                  down vote










                  up vote
                  1
                  down vote









                  I come up with a solution using Lagrange Multipliers (one of the comments and one of answers suggests to use this). First, we need to realize that the domain



                  $$ S ={ (x_1,x_2,cdots,x_n) in {mathbb{R}}^n | x_1 + x_2 + cdots + x_n = 0 text{ and } x_1^2 + x_2^2 + cdots + x_n^2 = 1} $$



                  is closed (since the functions $(x_1,x_2,cdots,x_n ) mapsto x_1 + x_2 + cdots + x_n$ and $(x_1,x_2,cdots,x_n ) mapsto x_1^2 + x_2^2 + cdots + x_n^2$ are continuous) and bounded (since $x_1^2 + x_2^2 + cdots + x_n^2 = 1$). Then S is compact.



                  Define
                  $$f(x_1,x_2,cdots,x_n ) = x_1^3 + x_2^3 + cdots+ x_n^3 $$
                  $$g(x_1,x_2,cdots,x_n ) = x_1^2 + x_2^2 + cdots+ x_n^2 -1 $$
                  $$h(x_1,x_2,cdots,x_n ) = x_1 + x_2 + cdots+ x_n $$



                  Since $S$ is compact and $f$ is continuous, then $f$ reaches a maximum value in
                  $S$. Using Lagrange Multiplier, we should have



                  $$nabla f = lambda_1 nabla g + lambda_2 nabla h$$



                  Thus,



                  $$ 3(x_1^2,x_2^2,cdots,x_n^2) = 2lambda_1(x_1,x_2,cdots,x_n ) + lambda_2(1,1,cdots,1)$$



                  Then,



                  $$3x_i^2 = 2lambda_1 x_i+ lambda_2 text{ } forall i in {1,2,cdots,n} label{1}tag{1}$$



                  If we add all the equations and use the constraints equations, we obtain $lambda_2= frac{3}{n}$. Then,



                  $$3x_i^2 = 2lambda_1 x_i+ frac{3}{n} $$



                  Solving for $x_i$, we obtain



                  $$ x_i = frac{lambda_1 pm sqrt{lambda_1^2 + frac{9}{n}}}{3} $$



                  Let $k$ be a natural number less or equal to $n$. Therefore we can suppose



                  $$ x_i = frac{lambda_1 + sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {1,2,cdots,k}$$



                  $$ x_i = frac{lambda_1 - sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {k+1,k+2,cdots,n}$$



                  Firtly, notice that $k$ is different of $0$ and $n$, because of the constraint $x_1+ x_2 + cdots + x_n =0$. Using the constraint $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ and after some simplifications, we obtain



                  $$lambda_1 left(nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} right) = 0label{2}tag{2}$$



                  And using $x_1+ x_2 + cdots + x_n =0$, we get



                  $$ nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} = 0 label{3}tag{3}$$



                  So, we just need to satisfy ref{3}, because with that ref{2} is automatically satisfied. Then, we get



                  $$lambda_1 = frac{3(n-2k)}{2sqrt{(n-k)nk}} $$



                  Notice that we should consider the other solution with the other sign, but in the end we obtain the same maximum value for both cases. Multiplying ref{1} by $x_i$:



                  $$ 3x_i^3 = 2lambda_1x_i^2 + frac{3x_i}{n} $$



                  Adding in all i's:



                  $$3f = 2lambda_1 $$



                  Thus,



                  $$ f(k) = frac{n-2k}{sqrt{(n-k)nk}} = frac{1}{sqrt{n}}left(sqrt{frac{n-k}{k}}-sqrt{frac{k}{n-k}}right)$$



                  Remember that $k in {2,3,cdots, n-1}$. Making $x = sqrt{frac{k}{n-k}}$ and analyzing the derivative of



                  $$y: (0,1) rightarrow mathbb{R}$$
                  $$ x mapstofrac{1}{x}-x $$



                  We see that the maximum value of $f$ is when $k=1$, that is,



                  $$ frac{n-2}{sqrt{(n-1)n}} $$



                  However, the Lagrange Multipliers just find the local extrema. How can I guarantee (if it's possible) that the above value is indeed the maximum value of $f$?






                  share|cite|improve this answer














                  I come up with a solution using Lagrange Multipliers (one of the comments and one of answers suggests to use this). First, we need to realize that the domain



                  $$ S ={ (x_1,x_2,cdots,x_n) in {mathbb{R}}^n | x_1 + x_2 + cdots + x_n = 0 text{ and } x_1^2 + x_2^2 + cdots + x_n^2 = 1} $$



                  is closed (since the functions $(x_1,x_2,cdots,x_n ) mapsto x_1 + x_2 + cdots + x_n$ and $(x_1,x_2,cdots,x_n ) mapsto x_1^2 + x_2^2 + cdots + x_n^2$ are continuous) and bounded (since $x_1^2 + x_2^2 + cdots + x_n^2 = 1$). Then S is compact.



                  Define
                  $$f(x_1,x_2,cdots,x_n ) = x_1^3 + x_2^3 + cdots+ x_n^3 $$
                  $$g(x_1,x_2,cdots,x_n ) = x_1^2 + x_2^2 + cdots+ x_n^2 -1 $$
                  $$h(x_1,x_2,cdots,x_n ) = x_1 + x_2 + cdots+ x_n $$



                  Since $S$ is compact and $f$ is continuous, then $f$ reaches a maximum value in
                  $S$. Using Lagrange Multiplier, we should have



                  $$nabla f = lambda_1 nabla g + lambda_2 nabla h$$



                  Thus,



                  $$ 3(x_1^2,x_2^2,cdots,x_n^2) = 2lambda_1(x_1,x_2,cdots,x_n ) + lambda_2(1,1,cdots,1)$$



                  Then,



                  $$3x_i^2 = 2lambda_1 x_i+ lambda_2 text{ } forall i in {1,2,cdots,n} label{1}tag{1}$$



                  If we add all the equations and use the constraints equations, we obtain $lambda_2= frac{3}{n}$. Then,



                  $$3x_i^2 = 2lambda_1 x_i+ frac{3}{n} $$



                  Solving for $x_i$, we obtain



                  $$ x_i = frac{lambda_1 pm sqrt{lambda_1^2 + frac{9}{n}}}{3} $$



                  Let $k$ be a natural number less or equal to $n$. Therefore we can suppose



                  $$ x_i = frac{lambda_1 + sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {1,2,cdots,k}$$



                  $$ x_i = frac{lambda_1 - sqrt{lambda_1^2 + frac{9}{n}}}{3} forall i in {k+1,k+2,cdots,n}$$



                  Firtly, notice that $k$ is different of $0$ and $n$, because of the constraint $x_1+ x_2 + cdots + x_n =0$. Using the constraint $x_1^2 + x_2^2 + cdots + x_n^2 = 1$ and after some simplifications, we obtain



                  $$lambda_1 left(nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} right) = 0label{2}tag{2}$$



                  And using $x_1+ x_2 + cdots + x_n =0$, we get



                  $$ nlambda_1 + (2k-n) sqrt{lambda_1^2 + frac{9}{n}} = 0 label{3}tag{3}$$



                  So, we just need to satisfy ref{3}, because with that ref{2} is automatically satisfied. Then, we get



                  $$lambda_1 = frac{3(n-2k)}{2sqrt{(n-k)nk}} $$



                  Notice that we should consider the other solution with the other sign, but in the end we obtain the same maximum value for both cases. Multiplying ref{1} by $x_i$:



                  $$ 3x_i^3 = 2lambda_1x_i^2 + frac{3x_i}{n} $$



                  Adding in all i's:



                  $$3f = 2lambda_1 $$



                  Thus,



                  $$ f(k) = frac{n-2k}{sqrt{(n-k)nk}} = frac{1}{sqrt{n}}left(sqrt{frac{n-k}{k}}-sqrt{frac{k}{n-k}}right)$$



                  Remember that $k in {2,3,cdots, n-1}$. Making $x = sqrt{frac{k}{n-k}}$ and analyzing the derivative of



                  $$y: (0,1) rightarrow mathbb{R}$$
                  $$ x mapstofrac{1}{x}-x $$



                  We see that the maximum value of $f$ is when $k=1$, that is,



                  $$ frac{n-2}{sqrt{(n-1)n}} $$



                  However, the Lagrange Multipliers just find the local extrema. How can I guarantee (if it's possible) that the above value is indeed the maximum value of $f$?







                  share|cite|improve this answer














                  share|cite|improve this answer



                  share|cite|improve this answer








                  edited Nov 23 at 13:58

























                  answered Nov 21 at 2:00









                  Rafael Deiga

                  657311




                  657311






























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