Find the maximum and minimum (there are four in total) that the function $f(x,y)=3xy$ achieves when $(x,y)$...












1














Find the maximum and minimum (there are four in total) that the function $f(x,y)=3xy$ achieves when $(x,y)$ travel the ellipse $x^2+y^2+xy=3$



I have thought about doing the following but I do not know if I am doing the right thing:



I'm using Lagrange multipliers and I start with $bigtriangledown f=lambdabigtriangledown g$, so from this I get $(3y,3x)=lambda(2x+y,2y+x)$ so $lambda=frac{3y}{2x+y}$ and $lambda=frac{3x}{2y+x}$ so $frac{3y}{2x+y}=frac{3x}{2y+x}$ so $x^2=y^2$ so $y=pm x$. So, I get to that $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and $(-sqrt{3},sqrt{3})$ are critical points, the problem is that I do not know how to determine if they are maximum or minimum, how can I do this? Thank you.










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  • 2




    Just compute f at those 4 points. Btw, (0,0) is not on the ellipse.
    – Martin R
    Jan 4 at 4:10












  • @MartinR Is what I did fine?
    – Nash
    Jan 4 at 20:55
















1














Find the maximum and minimum (there are four in total) that the function $f(x,y)=3xy$ achieves when $(x,y)$ travel the ellipse $x^2+y^2+xy=3$



I have thought about doing the following but I do not know if I am doing the right thing:



I'm using Lagrange multipliers and I start with $bigtriangledown f=lambdabigtriangledown g$, so from this I get $(3y,3x)=lambda(2x+y,2y+x)$ so $lambda=frac{3y}{2x+y}$ and $lambda=frac{3x}{2y+x}$ so $frac{3y}{2x+y}=frac{3x}{2y+x}$ so $x^2=y^2$ so $y=pm x$. So, I get to that $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and $(-sqrt{3},sqrt{3})$ are critical points, the problem is that I do not know how to determine if they are maximum or minimum, how can I do this? Thank you.










share|cite|improve this question




















  • 2




    Just compute f at those 4 points. Btw, (0,0) is not on the ellipse.
    – Martin R
    Jan 4 at 4:10












  • @MartinR Is what I did fine?
    – Nash
    Jan 4 at 20:55














1












1








1


2





Find the maximum and minimum (there are four in total) that the function $f(x,y)=3xy$ achieves when $(x,y)$ travel the ellipse $x^2+y^2+xy=3$



I have thought about doing the following but I do not know if I am doing the right thing:



I'm using Lagrange multipliers and I start with $bigtriangledown f=lambdabigtriangledown g$, so from this I get $(3y,3x)=lambda(2x+y,2y+x)$ so $lambda=frac{3y}{2x+y}$ and $lambda=frac{3x}{2y+x}$ so $frac{3y}{2x+y}=frac{3x}{2y+x}$ so $x^2=y^2$ so $y=pm x$. So, I get to that $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and $(-sqrt{3},sqrt{3})$ are critical points, the problem is that I do not know how to determine if they are maximum or minimum, how can I do this? Thank you.










share|cite|improve this question















Find the maximum and minimum (there are four in total) that the function $f(x,y)=3xy$ achieves when $(x,y)$ travel the ellipse $x^2+y^2+xy=3$



I have thought about doing the following but I do not know if I am doing the right thing:



I'm using Lagrange multipliers and I start with $bigtriangledown f=lambdabigtriangledown g$, so from this I get $(3y,3x)=lambda(2x+y,2y+x)$ so $lambda=frac{3y}{2x+y}$ and $lambda=frac{3x}{2y+x}$ so $frac{3y}{2x+y}=frac{3x}{2y+x}$ so $x^2=y^2$ so $y=pm x$. So, I get to that $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and $(-sqrt{3},sqrt{3})$ are critical points, the problem is that I do not know how to determine if they are maximum or minimum, how can I do this? Thank you.







real-analysis multivariable-calculus optimization lagrange-multiplier maxima-minima






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edited Jan 4 at 7:20









Michael Rozenberg

97.3k1589188




97.3k1589188










asked Jan 4 at 3:47









NashNash

48049




48049








  • 2




    Just compute f at those 4 points. Btw, (0,0) is not on the ellipse.
    – Martin R
    Jan 4 at 4:10












  • @MartinR Is what I did fine?
    – Nash
    Jan 4 at 20:55














  • 2




    Just compute f at those 4 points. Btw, (0,0) is not on the ellipse.
    – Martin R
    Jan 4 at 4:10












  • @MartinR Is what I did fine?
    – Nash
    Jan 4 at 20:55








2




2




Just compute f at those 4 points. Btw, (0,0) is not on the ellipse.
– Martin R
Jan 4 at 4:10






Just compute f at those 4 points. Btw, (0,0) is not on the ellipse.
– Martin R
Jan 4 at 4:10














@MartinR Is what I did fine?
– Nash
Jan 4 at 20:55




@MartinR Is what I did fine?
– Nash
Jan 4 at 20:55










7 Answers
7






active

oldest

votes


















3














If $x,y$ have the same sign $f(x,y)>0$



and if they have opposite signs $f(x,y)<0$



With the 4 pairs you have, 2 share the same sign and and 2 do not. That should make it clear wich are the maxima and which are the minima.



But is calculus even necessary?



Make this substitution



$x = u+v\
y = u-v$



Then your objective and constraint become:



$f(u,v) = 3u^2 - 3v^2\
3u^2 + v^2 = 3$



Plugging the constraint into the objective.



$f(u,v) = 3 - 4v^2$



$f$ is maximized when $v = 0$



$u = pm 1\
x = pm 1\
y = pm 1$



$f$ is minimized when $u = 0$



$v = pm sqrt 3\
x = pm sqrt 3\
y = mp sqrt 3$






share|cite|improve this answer























  • Is what I did fine?
    – Nash
    Jan 4 at 20:55






  • 1




    Your work on with the Lagrange multipliers was fine.
    – Doug M
    Jan 4 at 21:10



















2














Let $3xy=k$.



Thus,
$$k(x^2+y^2+xy)=9xy$$ or
$$kx^2+(k-9)xy+ky^2=0$$ has solutions.



Let $kneq0$.



Thus, $$(k-9)^2-4k^2geq0$$ or
$$(k+9)(k-3)leq0$$ or
$$-9leq kleq3.$$
Now, we see that $k=0$ is not relevant and the extreme value of $k$ occurs for $x=-frac{k-9}{2k}y$,



which says that
$$max_{x^2+y^2+xy=3}3xy=3$$ and
$$min_{x^2+y^2+xy=3}3xy=-9$$






share|cite|improve this answer





















  • Is what I did fine?
    – Nash
    Jan 4 at 20:56






  • 1




    @Nash Without the point $(0,0)$ it looks fine.
    – Michael Rozenberg
    Jan 4 at 20:58



















2














If Lagrange multipliers is not mandatory,



$$12=4x^2+4y^2+4xy=(2x+y)^2+(sqrt3y)^2$$



Let $sqrt3y=sqrt{12}cos tiff y=2cos t$



$2x+y=2sqrt3sin tiff 2x=2sqrt3sin t-2cos tiff x=sqrt3sin t-cos t=2sinleft(t-30^circright)$



$3xy=12cos tsinleft(t-30^circright)=6[sin(2t-30^circ)-sin30^circ]$



Now for real $x,y;t$ is real $$-1lesin(2t-30^circ)le1$$






share|cite|improve this answer





















  • Is what I did fine?
    – Nash
    Jan 4 at 20:56



















1














Evaluate your function $f(x,y) = 3xy$ at each of these four points, $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and compare the results.You will find maximum is achieved at $(1,1)$ and $(-1,-1)$ and minimum is achieved at $ pm (sqrt 3, -sqrt 3)$






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  • Is what I did fine?
    – Nash
    Jan 4 at 20:56










  • Yes your work is correct
    – Mohammad Riazi-Kermani
    2 days ago



















1














As the equations involved are homogeneous, making $y = lambda x$ we have



$$
min(max)3x^2lambda mbox{s. t. } x^2(1+lambda^2+lambda) = 3
$$



which is equivalent to



$$
min_{lambda}(max_{lambda})f(lambda) = frac{9lambda}{lambda^2+lambda+1}
$$



so the stationary points are at



$$
f'(lambda) = -frac{9 left(lambda ^2-1right)}{left(lambda ^2+lambda +1right)^2} = 0Rightarrow lambda^* = {-1,1}
$$



then the solutions are at $y = pm x$ etc.






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  • Is what I did fine?
    – Nash
    Jan 4 at 20:56



















1














1) $(x+y)^2- xy= 3;$



$xy = (x+y)^2 -3;$



$f(x,y)=3xy = 3(x+y)^2-9.$



$f_{min}=-9$, at $x=-y$;



$-3x^2= -9; x=pm √3; y=mp √3$;



2) $(x-y)^2+3xy= 3;$



$f(x,y)= 3xy= 3-(x-y)^2$;



$f_{max}= 3$, at $x=y$.



$x^2=1$; $x =pm 1$, $y=pm 1$.






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    0














    You can determine the "type" of the critical point using the partial derivatives. Suppose $(x,y)$ is a critical point that you are considering. Then,




    1. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} > 0$ when evaluated at $(x,y)$, then there is a relative minimum at $(x,y)$.

    2. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} < 0$, you have a relative minimum.

    3. If $f_{xx} f_{yy}- (f_{xx})^2 < 0$ then you've got a saddle point.

    4. If $f_{xx} f_{yy}- (f_{xx})^2 = 0$ its indeterminate.


    Edit: Just looked at the function you've provided. Clearly, it's going to indeterminate since the second derivative for $x$ or $y$ is $0$. You'll have to just plug in the values of the critical points you've found and determine it based on that.






    share|cite|improve this answer








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    • 1




      This only provides local maxima or minima of the restricted function. For global one uses compactness and this criterion is then useless.
      – Will M.
      Jan 4 at 4:36






    • 1




      Examining the Jacobian determinant of the objective function in a constrained optimization problem doesn’t usually tell you anything useful. This problem is a case in point: the Jacobian determinant is identically equal to $-9$, so per these criteria the only critical points are saddle points. This is true of $f$ on $mathbb R^2$, but certainly not of its restriction to the ellipse.
      – amd
      Jan 4 at 4:47











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    7 Answers
    7






    active

    oldest

    votes








    7 Answers
    7






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    3














    If $x,y$ have the same sign $f(x,y)>0$



    and if they have opposite signs $f(x,y)<0$



    With the 4 pairs you have, 2 share the same sign and and 2 do not. That should make it clear wich are the maxima and which are the minima.



    But is calculus even necessary?



    Make this substitution



    $x = u+v\
    y = u-v$



    Then your objective and constraint become:



    $f(u,v) = 3u^2 - 3v^2\
    3u^2 + v^2 = 3$



    Plugging the constraint into the objective.



    $f(u,v) = 3 - 4v^2$



    $f$ is maximized when $v = 0$



    $u = pm 1\
    x = pm 1\
    y = pm 1$



    $f$ is minimized when $u = 0$



    $v = pm sqrt 3\
    x = pm sqrt 3\
    y = mp sqrt 3$






    share|cite|improve this answer























    • Is what I did fine?
      – Nash
      Jan 4 at 20:55






    • 1




      Your work on with the Lagrange multipliers was fine.
      – Doug M
      Jan 4 at 21:10
















    3














    If $x,y$ have the same sign $f(x,y)>0$



    and if they have opposite signs $f(x,y)<0$



    With the 4 pairs you have, 2 share the same sign and and 2 do not. That should make it clear wich are the maxima and which are the minima.



    But is calculus even necessary?



    Make this substitution



    $x = u+v\
    y = u-v$



    Then your objective and constraint become:



    $f(u,v) = 3u^2 - 3v^2\
    3u^2 + v^2 = 3$



    Plugging the constraint into the objective.



    $f(u,v) = 3 - 4v^2$



    $f$ is maximized when $v = 0$



    $u = pm 1\
    x = pm 1\
    y = pm 1$



    $f$ is minimized when $u = 0$



    $v = pm sqrt 3\
    x = pm sqrt 3\
    y = mp sqrt 3$






    share|cite|improve this answer























    • Is what I did fine?
      – Nash
      Jan 4 at 20:55






    • 1




      Your work on with the Lagrange multipliers was fine.
      – Doug M
      Jan 4 at 21:10














    3












    3








    3






    If $x,y$ have the same sign $f(x,y)>0$



    and if they have opposite signs $f(x,y)<0$



    With the 4 pairs you have, 2 share the same sign and and 2 do not. That should make it clear wich are the maxima and which are the minima.



    But is calculus even necessary?



    Make this substitution



    $x = u+v\
    y = u-v$



    Then your objective and constraint become:



    $f(u,v) = 3u^2 - 3v^2\
    3u^2 + v^2 = 3$



    Plugging the constraint into the objective.



    $f(u,v) = 3 - 4v^2$



    $f$ is maximized when $v = 0$



    $u = pm 1\
    x = pm 1\
    y = pm 1$



    $f$ is minimized when $u = 0$



    $v = pm sqrt 3\
    x = pm sqrt 3\
    y = mp sqrt 3$






    share|cite|improve this answer














    If $x,y$ have the same sign $f(x,y)>0$



    and if they have opposite signs $f(x,y)<0$



    With the 4 pairs you have, 2 share the same sign and and 2 do not. That should make it clear wich are the maxima and which are the minima.



    But is calculus even necessary?



    Make this substitution



    $x = u+v\
    y = u-v$



    Then your objective and constraint become:



    $f(u,v) = 3u^2 - 3v^2\
    3u^2 + v^2 = 3$



    Plugging the constraint into the objective.



    $f(u,v) = 3 - 4v^2$



    $f$ is maximized when $v = 0$



    $u = pm 1\
    x = pm 1\
    y = pm 1$



    $f$ is minimized when $u = 0$



    $v = pm sqrt 3\
    x = pm sqrt 3\
    y = mp sqrt 3$







    share|cite|improve this answer














    share|cite|improve this answer



    share|cite|improve this answer








    edited Jan 4 at 7:24

























    answered Jan 4 at 4:25









    Doug MDoug M

    44.2k31854




    44.2k31854












    • Is what I did fine?
      – Nash
      Jan 4 at 20:55






    • 1




      Your work on with the Lagrange multipliers was fine.
      – Doug M
      Jan 4 at 21:10


















    • Is what I did fine?
      – Nash
      Jan 4 at 20:55






    • 1




      Your work on with the Lagrange multipliers was fine.
      – Doug M
      Jan 4 at 21:10
















    Is what I did fine?
    – Nash
    Jan 4 at 20:55




    Is what I did fine?
    – Nash
    Jan 4 at 20:55




    1




    1




    Your work on with the Lagrange multipliers was fine.
    – Doug M
    Jan 4 at 21:10




    Your work on with the Lagrange multipliers was fine.
    – Doug M
    Jan 4 at 21:10











    2














    Let $3xy=k$.



    Thus,
    $$k(x^2+y^2+xy)=9xy$$ or
    $$kx^2+(k-9)xy+ky^2=0$$ has solutions.



    Let $kneq0$.



    Thus, $$(k-9)^2-4k^2geq0$$ or
    $$(k+9)(k-3)leq0$$ or
    $$-9leq kleq3.$$
    Now, we see that $k=0$ is not relevant and the extreme value of $k$ occurs for $x=-frac{k-9}{2k}y$,



    which says that
    $$max_{x^2+y^2+xy=3}3xy=3$$ and
    $$min_{x^2+y^2+xy=3}3xy=-9$$






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56






    • 1




      @Nash Without the point $(0,0)$ it looks fine.
      – Michael Rozenberg
      Jan 4 at 20:58
















    2














    Let $3xy=k$.



    Thus,
    $$k(x^2+y^2+xy)=9xy$$ or
    $$kx^2+(k-9)xy+ky^2=0$$ has solutions.



    Let $kneq0$.



    Thus, $$(k-9)^2-4k^2geq0$$ or
    $$(k+9)(k-3)leq0$$ or
    $$-9leq kleq3.$$
    Now, we see that $k=0$ is not relevant and the extreme value of $k$ occurs for $x=-frac{k-9}{2k}y$,



    which says that
    $$max_{x^2+y^2+xy=3}3xy=3$$ and
    $$min_{x^2+y^2+xy=3}3xy=-9$$






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56






    • 1




      @Nash Without the point $(0,0)$ it looks fine.
      – Michael Rozenberg
      Jan 4 at 20:58














    2












    2








    2






    Let $3xy=k$.



    Thus,
    $$k(x^2+y^2+xy)=9xy$$ or
    $$kx^2+(k-9)xy+ky^2=0$$ has solutions.



    Let $kneq0$.



    Thus, $$(k-9)^2-4k^2geq0$$ or
    $$(k+9)(k-3)leq0$$ or
    $$-9leq kleq3.$$
    Now, we see that $k=0$ is not relevant and the extreme value of $k$ occurs for $x=-frac{k-9}{2k}y$,



    which says that
    $$max_{x^2+y^2+xy=3}3xy=3$$ and
    $$min_{x^2+y^2+xy=3}3xy=-9$$






    share|cite|improve this answer












    Let $3xy=k$.



    Thus,
    $$k(x^2+y^2+xy)=9xy$$ or
    $$kx^2+(k-9)xy+ky^2=0$$ has solutions.



    Let $kneq0$.



    Thus, $$(k-9)^2-4k^2geq0$$ or
    $$(k+9)(k-3)leq0$$ or
    $$-9leq kleq3.$$
    Now, we see that $k=0$ is not relevant and the extreme value of $k$ occurs for $x=-frac{k-9}{2k}y$,



    which says that
    $$max_{x^2+y^2+xy=3}3xy=3$$ and
    $$min_{x^2+y^2+xy=3}3xy=-9$$







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Jan 4 at 7:16









    Michael RozenbergMichael Rozenberg

    97.3k1589188




    97.3k1589188












    • Is what I did fine?
      – Nash
      Jan 4 at 20:56






    • 1




      @Nash Without the point $(0,0)$ it looks fine.
      – Michael Rozenberg
      Jan 4 at 20:58


















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56






    • 1




      @Nash Without the point $(0,0)$ it looks fine.
      – Michael Rozenberg
      Jan 4 at 20:58
















    Is what I did fine?
    – Nash
    Jan 4 at 20:56




    Is what I did fine?
    – Nash
    Jan 4 at 20:56




    1




    1




    @Nash Without the point $(0,0)$ it looks fine.
    – Michael Rozenberg
    Jan 4 at 20:58




    @Nash Without the point $(0,0)$ it looks fine.
    – Michael Rozenberg
    Jan 4 at 20:58











    2














    If Lagrange multipliers is not mandatory,



    $$12=4x^2+4y^2+4xy=(2x+y)^2+(sqrt3y)^2$$



    Let $sqrt3y=sqrt{12}cos tiff y=2cos t$



    $2x+y=2sqrt3sin tiff 2x=2sqrt3sin t-2cos tiff x=sqrt3sin t-cos t=2sinleft(t-30^circright)$



    $3xy=12cos tsinleft(t-30^circright)=6[sin(2t-30^circ)-sin30^circ]$



    Now for real $x,y;t$ is real $$-1lesin(2t-30^circ)le1$$






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56
















    2














    If Lagrange multipliers is not mandatory,



    $$12=4x^2+4y^2+4xy=(2x+y)^2+(sqrt3y)^2$$



    Let $sqrt3y=sqrt{12}cos tiff y=2cos t$



    $2x+y=2sqrt3sin tiff 2x=2sqrt3sin t-2cos tiff x=sqrt3sin t-cos t=2sinleft(t-30^circright)$



    $3xy=12cos tsinleft(t-30^circright)=6[sin(2t-30^circ)-sin30^circ]$



    Now for real $x,y;t$ is real $$-1lesin(2t-30^circ)le1$$






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56














    2












    2








    2






    If Lagrange multipliers is not mandatory,



    $$12=4x^2+4y^2+4xy=(2x+y)^2+(sqrt3y)^2$$



    Let $sqrt3y=sqrt{12}cos tiff y=2cos t$



    $2x+y=2sqrt3sin tiff 2x=2sqrt3sin t-2cos tiff x=sqrt3sin t-cos t=2sinleft(t-30^circright)$



    $3xy=12cos tsinleft(t-30^circright)=6[sin(2t-30^circ)-sin30^circ]$



    Now for real $x,y;t$ is real $$-1lesin(2t-30^circ)le1$$






    share|cite|improve this answer












    If Lagrange multipliers is not mandatory,



    $$12=4x^2+4y^2+4xy=(2x+y)^2+(sqrt3y)^2$$



    Let $sqrt3y=sqrt{12}cos tiff y=2cos t$



    $2x+y=2sqrt3sin tiff 2x=2sqrt3sin t-2cos tiff x=sqrt3sin t-cos t=2sinleft(t-30^circright)$



    $3xy=12cos tsinleft(t-30^circright)=6[sin(2t-30^circ)-sin30^circ]$



    Now for real $x,y;t$ is real $$-1lesin(2t-30^circ)le1$$







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Jan 4 at 11:47









    lab bhattacharjeelab bhattacharjee

    223k15156274




    223k15156274












    • Is what I did fine?
      – Nash
      Jan 4 at 20:56


















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56
















    Is what I did fine?
    – Nash
    Jan 4 at 20:56




    Is what I did fine?
    – Nash
    Jan 4 at 20:56











    1














    Evaluate your function $f(x,y) = 3xy$ at each of these four points, $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and compare the results.You will find maximum is achieved at $(1,1)$ and $(-1,-1)$ and minimum is achieved at $ pm (sqrt 3, -sqrt 3)$






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56










    • Yes your work is correct
      – Mohammad Riazi-Kermani
      2 days ago
















    1














    Evaluate your function $f(x,y) = 3xy$ at each of these four points, $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and compare the results.You will find maximum is achieved at $(1,1)$ and $(-1,-1)$ and minimum is achieved at $ pm (sqrt 3, -sqrt 3)$






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56










    • Yes your work is correct
      – Mohammad Riazi-Kermani
      2 days ago














    1












    1








    1






    Evaluate your function $f(x,y) = 3xy$ at each of these four points, $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and compare the results.You will find maximum is achieved at $(1,1)$ and $(-1,-1)$ and minimum is achieved at $ pm (sqrt 3, -sqrt 3)$






    share|cite|improve this answer












    Evaluate your function $f(x,y) = 3xy$ at each of these four points, $(0,0), (1,1), (-1,-1), (sqrt{3},-sqrt{3})$ and compare the results.You will find maximum is achieved at $(1,1)$ and $(-1,-1)$ and minimum is achieved at $ pm (sqrt 3, -sqrt 3)$







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Jan 4 at 4:17









    Mohammad Riazi-KermaniMohammad Riazi-Kermani

    41k42059




    41k42059












    • Is what I did fine?
      – Nash
      Jan 4 at 20:56










    • Yes your work is correct
      – Mohammad Riazi-Kermani
      2 days ago


















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56










    • Yes your work is correct
      – Mohammad Riazi-Kermani
      2 days ago
















    Is what I did fine?
    – Nash
    Jan 4 at 20:56




    Is what I did fine?
    – Nash
    Jan 4 at 20:56












    Yes your work is correct
    – Mohammad Riazi-Kermani
    2 days ago




    Yes your work is correct
    – Mohammad Riazi-Kermani
    2 days ago











    1














    As the equations involved are homogeneous, making $y = lambda x$ we have



    $$
    min(max)3x^2lambda mbox{s. t. } x^2(1+lambda^2+lambda) = 3
    $$



    which is equivalent to



    $$
    min_{lambda}(max_{lambda})f(lambda) = frac{9lambda}{lambda^2+lambda+1}
    $$



    so the stationary points are at



    $$
    f'(lambda) = -frac{9 left(lambda ^2-1right)}{left(lambda ^2+lambda +1right)^2} = 0Rightarrow lambda^* = {-1,1}
    $$



    then the solutions are at $y = pm x$ etc.






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56
















    1














    As the equations involved are homogeneous, making $y = lambda x$ we have



    $$
    min(max)3x^2lambda mbox{s. t. } x^2(1+lambda^2+lambda) = 3
    $$



    which is equivalent to



    $$
    min_{lambda}(max_{lambda})f(lambda) = frac{9lambda}{lambda^2+lambda+1}
    $$



    so the stationary points are at



    $$
    f'(lambda) = -frac{9 left(lambda ^2-1right)}{left(lambda ^2+lambda +1right)^2} = 0Rightarrow lambda^* = {-1,1}
    $$



    then the solutions are at $y = pm x$ etc.






    share|cite|improve this answer





















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56














    1












    1








    1






    As the equations involved are homogeneous, making $y = lambda x$ we have



    $$
    min(max)3x^2lambda mbox{s. t. } x^2(1+lambda^2+lambda) = 3
    $$



    which is equivalent to



    $$
    min_{lambda}(max_{lambda})f(lambda) = frac{9lambda}{lambda^2+lambda+1}
    $$



    so the stationary points are at



    $$
    f'(lambda) = -frac{9 left(lambda ^2-1right)}{left(lambda ^2+lambda +1right)^2} = 0Rightarrow lambda^* = {-1,1}
    $$



    then the solutions are at $y = pm x$ etc.






    share|cite|improve this answer












    As the equations involved are homogeneous, making $y = lambda x$ we have



    $$
    min(max)3x^2lambda mbox{s. t. } x^2(1+lambda^2+lambda) = 3
    $$



    which is equivalent to



    $$
    min_{lambda}(max_{lambda})f(lambda) = frac{9lambda}{lambda^2+lambda+1}
    $$



    so the stationary points are at



    $$
    f'(lambda) = -frac{9 left(lambda ^2-1right)}{left(lambda ^2+lambda +1right)^2} = 0Rightarrow lambda^* = {-1,1}
    $$



    then the solutions are at $y = pm x$ etc.







    share|cite|improve this answer












    share|cite|improve this answer



    share|cite|improve this answer










    answered Jan 4 at 12:16









    CesareoCesareo

    8,3413516




    8,3413516












    • Is what I did fine?
      – Nash
      Jan 4 at 20:56


















    • Is what I did fine?
      – Nash
      Jan 4 at 20:56
















    Is what I did fine?
    – Nash
    Jan 4 at 20:56




    Is what I did fine?
    – Nash
    Jan 4 at 20:56











    1














    1) $(x+y)^2- xy= 3;$



    $xy = (x+y)^2 -3;$



    $f(x,y)=3xy = 3(x+y)^2-9.$



    $f_{min}=-9$, at $x=-y$;



    $-3x^2= -9; x=pm √3; y=mp √3$;



    2) $(x-y)^2+3xy= 3;$



    $f(x,y)= 3xy= 3-(x-y)^2$;



    $f_{max}= 3$, at $x=y$.



    $x^2=1$; $x =pm 1$, $y=pm 1$.






    share|cite|improve this answer




























      1














      1) $(x+y)^2- xy= 3;$



      $xy = (x+y)^2 -3;$



      $f(x,y)=3xy = 3(x+y)^2-9.$



      $f_{min}=-9$, at $x=-y$;



      $-3x^2= -9; x=pm √3; y=mp √3$;



      2) $(x-y)^2+3xy= 3;$



      $f(x,y)= 3xy= 3-(x-y)^2$;



      $f_{max}= 3$, at $x=y$.



      $x^2=1$; $x =pm 1$, $y=pm 1$.






      share|cite|improve this answer


























        1












        1








        1






        1) $(x+y)^2- xy= 3;$



        $xy = (x+y)^2 -3;$



        $f(x,y)=3xy = 3(x+y)^2-9.$



        $f_{min}=-9$, at $x=-y$;



        $-3x^2= -9; x=pm √3; y=mp √3$;



        2) $(x-y)^2+3xy= 3;$



        $f(x,y)= 3xy= 3-(x-y)^2$;



        $f_{max}= 3$, at $x=y$.



        $x^2=1$; $x =pm 1$, $y=pm 1$.






        share|cite|improve this answer














        1) $(x+y)^2- xy= 3;$



        $xy = (x+y)^2 -3;$



        $f(x,y)=3xy = 3(x+y)^2-9.$



        $f_{min}=-9$, at $x=-y$;



        $-3x^2= -9; x=pm √3; y=mp √3$;



        2) $(x-y)^2+3xy= 3;$



        $f(x,y)= 3xy= 3-(x-y)^2$;



        $f_{max}= 3$, at $x=y$.



        $x^2=1$; $x =pm 1$, $y=pm 1$.







        share|cite|improve this answer














        share|cite|improve this answer



        share|cite|improve this answer








        edited Jan 4 at 21:12

























        answered Jan 4 at 7:22









        Peter SzilasPeter Szilas

        10.8k2720




        10.8k2720























            0














            You can determine the "type" of the critical point using the partial derivatives. Suppose $(x,y)$ is a critical point that you are considering. Then,




            1. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} > 0$ when evaluated at $(x,y)$, then there is a relative minimum at $(x,y)$.

            2. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} < 0$, you have a relative minimum.

            3. If $f_{xx} f_{yy}- (f_{xx})^2 < 0$ then you've got a saddle point.

            4. If $f_{xx} f_{yy}- (f_{xx})^2 = 0$ its indeterminate.


            Edit: Just looked at the function you've provided. Clearly, it's going to indeterminate since the second derivative for $x$ or $y$ is $0$. You'll have to just plug in the values of the critical points you've found and determine it based on that.






            share|cite|improve this answer








            New contributor




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














            • 1




              This only provides local maxima or minima of the restricted function. For global one uses compactness and this criterion is then useless.
              – Will M.
              Jan 4 at 4:36






            • 1




              Examining the Jacobian determinant of the objective function in a constrained optimization problem doesn’t usually tell you anything useful. This problem is a case in point: the Jacobian determinant is identically equal to $-9$, so per these criteria the only critical points are saddle points. This is true of $f$ on $mathbb R^2$, but certainly not of its restriction to the ellipse.
              – amd
              Jan 4 at 4:47
















            0














            You can determine the "type" of the critical point using the partial derivatives. Suppose $(x,y)$ is a critical point that you are considering. Then,




            1. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} > 0$ when evaluated at $(x,y)$, then there is a relative minimum at $(x,y)$.

            2. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} < 0$, you have a relative minimum.

            3. If $f_{xx} f_{yy}- (f_{xx})^2 < 0$ then you've got a saddle point.

            4. If $f_{xx} f_{yy}- (f_{xx})^2 = 0$ its indeterminate.


            Edit: Just looked at the function you've provided. Clearly, it's going to indeterminate since the second derivative for $x$ or $y$ is $0$. You'll have to just plug in the values of the critical points you've found and determine it based on that.






            share|cite|improve this answer








            New contributor




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














            • 1




              This only provides local maxima or minima of the restricted function. For global one uses compactness and this criterion is then useless.
              – Will M.
              Jan 4 at 4:36






            • 1




              Examining the Jacobian determinant of the objective function in a constrained optimization problem doesn’t usually tell you anything useful. This problem is a case in point: the Jacobian determinant is identically equal to $-9$, so per these criteria the only critical points are saddle points. This is true of $f$ on $mathbb R^2$, but certainly not of its restriction to the ellipse.
              – amd
              Jan 4 at 4:47














            0












            0








            0






            You can determine the "type" of the critical point using the partial derivatives. Suppose $(x,y)$ is a critical point that you are considering. Then,




            1. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} > 0$ when evaluated at $(x,y)$, then there is a relative minimum at $(x,y)$.

            2. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} < 0$, you have a relative minimum.

            3. If $f_{xx} f_{yy}- (f_{xx})^2 < 0$ then you've got a saddle point.

            4. If $f_{xx} f_{yy}- (f_{xx})^2 = 0$ its indeterminate.


            Edit: Just looked at the function you've provided. Clearly, it's going to indeterminate since the second derivative for $x$ or $y$ is $0$. You'll have to just plug in the values of the critical points you've found and determine it based on that.






            share|cite|improve this answer








            New contributor




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









            You can determine the "type" of the critical point using the partial derivatives. Suppose $(x,y)$ is a critical point that you are considering. Then,




            1. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} > 0$ when evaluated at $(x,y)$, then there is a relative minimum at $(x,y)$.

            2. If $f_{xx} f_{yy}- (f_{xx})^2 > 0$ and $f_{xx} < 0$, you have a relative minimum.

            3. If $f_{xx} f_{yy}- (f_{xx})^2 < 0$ then you've got a saddle point.

            4. If $f_{xx} f_{yy}- (f_{xx})^2 = 0$ its indeterminate.


            Edit: Just looked at the function you've provided. Clearly, it's going to indeterminate since the second derivative for $x$ or $y$ is $0$. You'll have to just plug in the values of the critical points you've found and determine it based on that.







            share|cite|improve this answer








            New contributor




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









            share|cite|improve this answer



            share|cite|improve this answer






            New contributor




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









            answered Jan 4 at 4:13









            kkckkc

            1308




            1308




            New contributor




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





            New contributor





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






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








            • 1




              This only provides local maxima or minima of the restricted function. For global one uses compactness and this criterion is then useless.
              – Will M.
              Jan 4 at 4:36






            • 1




              Examining the Jacobian determinant of the objective function in a constrained optimization problem doesn’t usually tell you anything useful. This problem is a case in point: the Jacobian determinant is identically equal to $-9$, so per these criteria the only critical points are saddle points. This is true of $f$ on $mathbb R^2$, but certainly not of its restriction to the ellipse.
              – amd
              Jan 4 at 4:47














            • 1




              This only provides local maxima or minima of the restricted function. For global one uses compactness and this criterion is then useless.
              – Will M.
              Jan 4 at 4:36






            • 1




              Examining the Jacobian determinant of the objective function in a constrained optimization problem doesn’t usually tell you anything useful. This problem is a case in point: the Jacobian determinant is identically equal to $-9$, so per these criteria the only critical points are saddle points. This is true of $f$ on $mathbb R^2$, but certainly not of its restriction to the ellipse.
              – amd
              Jan 4 at 4:47








            1




            1




            This only provides local maxima or minima of the restricted function. For global one uses compactness and this criterion is then useless.
            – Will M.
            Jan 4 at 4:36




            This only provides local maxima or minima of the restricted function. For global one uses compactness and this criterion is then useless.
            – Will M.
            Jan 4 at 4:36




            1




            1




            Examining the Jacobian determinant of the objective function in a constrained optimization problem doesn’t usually tell you anything useful. This problem is a case in point: the Jacobian determinant is identically equal to $-9$, so per these criteria the only critical points are saddle points. This is true of $f$ on $mathbb R^2$, but certainly not of its restriction to the ellipse.
            – amd
            Jan 4 at 4:47




            Examining the Jacobian determinant of the objective function in a constrained optimization problem doesn’t usually tell you anything useful. This problem is a case in point: the Jacobian determinant is identically equal to $-9$, so per these criteria the only critical points are saddle points. This is true of $f$ on $mathbb R^2$, but certainly not of its restriction to the ellipse.
            – amd
            Jan 4 at 4:47


















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