Multivariable calculus

Critical Points of (x^2 + y^2) e^(-x)

f(x,y) = (x^2+y^2)e^(-x) has exactly two critical points. The origin is a local minimum with D = 4 and value 0. The point (2,0) is a saddle with D = -4/e^4, about -0.0733. Since e^(-x) is never zero, f_y = 2y e^(-x) forces y = 0, and then f_x = 0 reduces to x(2 - x) = 0.

f(x,y)=(x2+y2)exf(x,y) = \left(x^{2}+y^{2}\right)e^{-x}
  • (0,0)\left(0, 0\right) local minimumdiscriminant D = 4
  • (2,0)\left(2, 0\right) saddle pointdiscriminant D = -0.07326255555493671

Solve the easy equation first

Only the xx derivative needs the product rule, because exe^{-x} is a constant as far as yy is concerned.

fx=ex(2xx2y2),fy=2yex\frac{\partial f}{\partial x} = e^{-x}\left(2x - x^{2} - y^{2}\right), \qquad \frac{\partial f}{\partial y} = 2y\,e^{-x}

Start with the second equation, since it is the simpler of the two. The exponential is never zero, so 2yex=02y\,e^{-x} = 0 forces y=0y = 0 and nothing else. That is worth doing before touching the first equation: substituting y=0y = 0 collapses it from two variables to one.

ex(2xx2)=0    x(2x)=0    x=0  or  x=2e^{-x}\left(2x - x^{2}\right) = 0 \implies x(2 - x) = 0 \implies x = 0 \ \text{ or } \ x = 2

Exactly two critical points, (0,0)(0,0) and (2,0)(2,0), and since every step was an equivalence rather than an approximation, the list is complete.

Now the second partials. Differentiating fxf_x again in xx needs the product rule once more; the other two are quick.

fxx=ex(x2+y24x+2),fyy=2ex,fxy=2yexf_{xx} = e^{-x}\left(x^{2}+y^{2}-4x+2\right), \qquad f_{yy} = 2e^{-x}, \qquad f_{xy} = -2y\,e^{-x}

A minimum you can prove twice, and a saddle you can see

At the origin, fxx=2f_{xx} = 2, fyy=2f_{yy} = 2 and fxy=0f_{xy} = 0, so D=4>0D = 4 > 0 with a positive diagonal entry: a local minimum with f(0,0)=0f(0,0) = 0. A second argument confirms it without any derivatives: x2+y20x^{2}+y^{2} \ge 0 and ex>0e^{-x} > 0, so f0f \ge 0 everywhere and equals 0 only at the origin. The local minimum is in fact the global minimum.

At (2,0)(2,0), fxy=0f_{xy} = 0 because y=0y = 0, while fxx=e2(48+2)=2e2f_{xx} = e^{-2}(4 - 8 + 2) = -\frac{2}{e^{2}} and fyy=2e2f_{yy} = \frac{2}{e^{2}}. The two diagonal entries have opposite signs, so their product is negative and D=4e40.0733D = -\frac{4}{e^{4}} \approx -0.0733: a saddle.

Opposite signs on the diagonal is the clearest saddle there is, because you can see both directions separately. Along the xx-axis, f(x,0)=x2exf(x, 0) = x^{2}e^{-x}, which rises from 0, peaks at x=2x = 2 with value 4e20.541\frac{4}{e^{2}} \approx 0.541, then decays. So (2,0)(2,0) is a maximum along that line.

f(2,y)=(4+y2)e2f(2, y) = \left(4 + y^{2}\right)e^{-2}

Along the vertical line x=2x = 2, the same point is the bottom of an upward parabola. Highest point in one direction, lowest in another: that is a saddle, and the algebra and the picture agree.

The mistake students make

The minus sign in the exponent is where this problem is won or lost. Differentiating exe^{-x} gives ex-e^{-x}, so the product rule produces a subtraction, not an addition.

fx=2xex+(x2+y2)(ex)=ex(2xx2y2)\frac{\partial f}{\partial x} = 2x\,e^{-x} + \left(x^{2}+y^{2}\right)\left(-e^{-x}\right) = e^{-x}\left(2x - x^{2} - y^{2}\right)

Miss it and you get 2x+x2+y2=02x + x^{2} + y^{2} = 0, whose solutions with y=0y = 0 are x=0x = 0 and x=2x = -2. The reported saddle then lands at (2,0)(-2, 0), which is not a critical point at all: the true derivative there is e2(44)=8e2e^{2}(-4 - 4) = -8e^{2}, nowhere near zero. A sign slip does not merely shift the answer, it invents a point.

  • Differentiate the exponential factor on its own and note the sign it carries.
  • Assemble the product rule, then factor exe^{-x} back out.
  • Substitute your candidate point into the original partials and confirm both come out to zero.

The other habit worth building is choosing which equation to solve first. Attacking 2xx2y2=02x - x^{2} - y^{2} = 0 before using fy=0f_y = 0 leaves a circle of candidate points and a much messier substitution. The equation with fewer terms usually carries more information, and here fy=0f_y = 0 pins yy down completely in one line.

Frequently asked questions

Why is the discriminant so small at (2,0)?

Because the exponential factor e2e^{-2} appears in both diagonal second partials, so the discriminant carries e40.0183e^{-4} \approx 0.0183. The size of DD carries no meaning on its own; only its sign enters the classification. A discriminant of 0.0733-0.0733 is exactly as decisive as one of 1000-1000, and comparing magnitudes between different points or different surfaces tells you nothing about how strong a saddle is.

Does f have a global maximum?

No. Along the negative xx-axis, f(x,0)=x2exf(x, 0) = x^{2}e^{-x} grows without bound as xx decreases, since exe^{-x} blows up while x2x^{2} also grows. The surface is unbounded above, which is consistent with having only one local minimum and one saddle and no local maximum anywhere. Note that the saddle at (2,0)(2,0) is the highest point along the xx-axis to its right, but it is nothing like a maximum of the surface, since ff increases in the yy direction there.