Maximize $x^2 + y^2$ subject to $x^2 + y^2 = 1 - xy$

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The equation $x^2 + y^2 = 1 - xy$ represents an ellipse. I am trying to show that its major axis is along $y=-x$ and find the vertex. To find the vertex I tried to find the vector in the ellipse with the greatest norm, which is equivalent to maximizing $x^2 + y^2$ subject to $x^2 + y^2 = 1 - xy$. How can I approach this?

(The maximum value of the objective function $x^2 + y^2$ seems to be 2. If I start out by assuming this I am able to show that the function cannot exceed this value. But this is kind of doing it in reverse as I start by evaluating the function at $y=-x=1.$ This also doesn't prove that $y=-x$ is the only optimal solution. How would I do it without "guessing" the solution first?)

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If$$f(x,y)=x^2+y^2\quad\text{and}\quad g(x,y)=x^2+y^2+xy,$$then you want to know the maximum of $f$ under the restriction that $g(x,y)=1$. In order to do so, you can solve the system$$\left\{\begin{array}{l}f_x(a,b)=\lambda g_x(a,b)\\f_y(a,b)=\lambda g_y(a,b)\\g(a,b)=1\end{array}\right.\iff\left\{\begin{array}{l}2a=2\lambda a+\lambda b\\2b=2\lambda b+\lambda a\\a^2+b^2-ab=1\end{array}\right.$$The first two equations are a system of two linear equations, depending upon a parameter $\lambda$:$$\left\{\begin{array}{l}(2-2\lambda)a-\lambda b=0\\-\lambda a+(2-2\lambda)b=0.\end{array}\right.\label{a}\tag1$$The determinant of the matrix of coefficients of the system \eqref{a} is $3\lambda^2-8\lambda+4$ whose roots are $2$ and $\frac23$. If $\lambda$ is not one of these numbers, then the only solution of \eqref{a} is $(0,0)$, but $g(0,0)\ne1$. So, see what happens if $\lambda$ is one of those numbers:

  • if $\lambda=2$, then the solutions are $\pm(1,-1)$;
  • if $\lambda=\frac23$, then the solutions are $\pm\left(\frac1{\sqrt3},\frac1{\sqrt3}\right)$.

Since the value that $f$ takes at the first two points is $2$ and the values that it takes at the other two is $\frac23$, the maximum of $f$ is indeed $2$.

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If you would like to avoid calculus and use linear algebra instead (to each their own!), let us write down this equation in a different way.

With matrices, if we set

$$ A = \begin{pmatrix} 1 & \frac{1}{2} \\ \frac{1}{2}& 1 \end{pmatrix}$$ then this equation is the same as $\textbf{x}^TA \textbf{x} = 1$ with $\textbf{x} = (x ,y)^T$.

Now, using linear algebra we know symmetric matrices can be orthogonally diagonalized so we get

$A = PDP^T = PDP^{-1}$ where $$P = \begin{pmatrix} -1 & 1 \\ 1& 1 \end{pmatrix} $$ $$D = \begin{pmatrix} \frac{1}{2} & 0 \\ 0 & \frac{3}{2} \end{pmatrix}$$

So, this says that the major and minor axes of this quadratic are along $(-1, 1)^T$ and $(1,1)^T$ so exactly along the lines $y = x$ and $y= -x$.

The reason this works is that in the coordinates given by $P$ (or rather $P^T$), you end up with the equation of a circle in standard position i.e. something like $u^2 + v^2 = 1$ where $(u, v)^T = P^T (x,y)^T$. So $P$ gives you the directions along which to transform the circle into an ellipse, and $D$ tells you by how much to stretch along those axes.

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$\Delta \thinspace \thinspace \rm {method \thinspace\thinspace works\thinspace .}$

You have :

$$ \begin{cases}x^2+y^2=a,\thinspace a\in\mathbb R\\ x^2+y^2+xy=1\end{cases} $$

This implies that,

$$ \begin{cases}y=\frac {1-a}{x}\\ x^2+\frac{(a-1)^2}{x^2}-a=0\end{cases} $$

Then, letting $x^2=u$, we have :

$$ \begin{align}&u+\frac {(a-1)^2}{u}-a=0\\ \implies &u^2-ua+(a-1)^2=0\end{align} $$

This leads to:

$$ \begin{align}&\Delta_u=a^2-4(a-1)^2\geq 0\\ \implies &\frac 23\leq a\leq 2\thinspace .\end{align} $$

The equality $a=2$ holds, iff when :

$$\begin{align}&u=\frac a2=1\\ \implies &(x,y)=\left(\pm 1,\mp 1\right)\end{align}$$

Thus, we obtain a global maximum :

$$ \begin{align}\max\left\{x^2+y^2\mid x^2+y^2=1-xy\right\}&=2\\ \rm {at} \thinspace\thinspace\thinspace (x,y)=\left(\pm 1\thinspace,\mp 1\right)\thinspace\thinspace\thinspace\thinspace\thinspace\thinspace\thinspace\thinspace\thinspace\thinspace\thinspace\end{align} $$

Similarly, since $a\geq \frac 23$, then we obtain a global minimum :

$$ \begin{align}\min\left\{x^2+y^2\mid x^2+y^2=1-xy\right\}&=\frac 23\\ \rm {at} \thinspace\thinspace\thinspace (x,y)=\left(\pm \frac {\sqrt 3}{3},\thinspace \pm \frac {\sqrt 3}{3}\right)\end{align} $$

This completes the answer .

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Using polar coordinates, then

$ x = r \cos \theta $

$ y = r \sin \theta $

So we want to maximize $ r^2 = x^2 + y^2 $ subject to $ r^2 = 1 - \dfrac{1}{2} r^2 \sin(2 \theta) $

Hence,

$ r^2 = \dfrac{1}{1 + \frac{1}{2} \sin(2 \theta) } $

Clearly the maximum is when $ \sin(2 \theta) = -1 $, which corresponds to $ \theta = \dfrac{3 \pi}{4} $ and $ \theta = \dfrac{7 \pi}{4} $

At which points , $ r^2 = 2 $ (and this is the maximum value) and for the first value of $\theta$, we get

$ x = -1 , y = 1 $

And for the second value of $\theta$ we get

$ x = 1, y = -1 $

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Substitutions work much faster .

Let $\thinspace x=a+b,\thinspace y=a-b$, then you have :

$$\begin{align}&x^2+y^2=1-xy\\ \iff &3a^2+b^2=1\end{align}$$

This leads to the following :

$\underline{\rm {Global\thinspace\thinspace maximum}}$

$$\begin{align}x^2+y^2&=2(a^2+b^2)\\ &=2(a^2+1-3a^2)\\ &=2-4a^2\leq 2\end{align}$$

Equality occurs iff, when $a=0,\thinspace |b|=1$, which corresponds to $\left(x,y\right)=\left(\pm 1,\mp 1\right)\thinspace . $

$\underline {\rm {Global\thinspace\thinspace minimum }}$

$$\begin{align}x^2+y^2&=2(a^2+b^2)\\ &=2\left(\frac {1-b^2}{3}+b^2\right)\\ &=\frac 23\left(2b^2+1\right)\geq\frac 23\end{align}$$

Equality occurs iff, when $b=0,\thinspace |a|=\frac {\sqrt 3}{3}$, which corresponds to $\left(x,y\right)=\left(\pm \frac {\sqrt 3}{3},\pm \frac {\sqrt 3}{3}\right).$

This completes the answer.

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If looking at a generalization is interesting the following:

Let $(f,g)$ be given object and constraint function respectively, Euler-Lagrange equation (in calculus of variations approach) can be also expressed as

$$\frac{f_{x}}{ f_{y}}=\frac{g_{x}}{ g_{y}}$$

$$\frac{2x}{2y}=\frac{2x+y}{2y+x}$$

which simplifies to

$$ x+y=0,~ x-y=0~ $$

The straight line pair intersects the constraint function graph $g(x,y)$ (green) at four points solved which are shown at minimum and maximum respectively :

$$ D1=(\frac{ 1}{ \sqrt 3},\frac{ 1}{ \sqrt 3}),D2=(\frac{- 1}{ \sqrt 3},\frac{- 1}{ \sqrt 3}), ~ E1=(-1,1),E2=( 1,-1); $$

enter image description here

The choice between the extrema is decided by the Hessian with second partial derivatives. This approach gives insight also to the sign of Gauss curvature of the constraint function

$$z= g (x,y)$$

at these points in 3-space.

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A conic can be brought to canonical form with only one translation and one rotation. In this example the center of the conic is $(0,0)$, so we need only a rotation. Since $x^2-xy+y^2=1$ is symmetric in $x$ and $y$, without the formula, the rotation angle is easily $45^{\circ}$. Let, $P=(x,y)$ be old coordinates and $P'=(x',y')$ be new coordinates and $R_{\theta}$ be rotation transformation about the origin $\theta$ degrees. Then $P'=R_{45^{\circ}}P$ or in other words, $P=R_{-45^{\circ}}P'$ which gives $$(x,y)=(\frac{1}{\sqrt2}x'+\frac{1}{\sqrt2}y',-\frac{1}{\sqrt2}x'+\frac{1}{\sqrt2}y').$$ Substituting this in the ellipse equation and omiting dashes easily gives $$\frac{x^2}{2}+\frac{y^2}{2/3}=1.$$ The vertices of this new ellipse is $\pm(\sqrt2,0)$ and $\pm(0,\frac{\sqrt2}{\sqrt3})$. Now we have to rotate back these vertices by $R_{-45^{\circ}}=\left[\begin{matrix}\cos(-45^{\circ})&-\sin(-45^{\circ})\\\sin(-45^{\circ})&\cos(-45^{\circ})\end{matrix}\right]$. We get $\pm(1,-1)$ and $\pm(\frac{1}{\sqrt3},\frac{1}{\sqrt3})$.