M408M Learning Module Pages
Main page
Chapter 10: Parametric Equations
and Polar Coordinates
Chapter 12: Vectors and the Geometry of Space
Chapter 13: Vector Functions
Chapter 14: Partial Derivatives
Learning module LM 14.1:
Functions of 2 or 3 variables:
Learning module LM 14.3:
Partial derivatives:
Learning module LM 14.4:
Tangent planes and linear approximations:
Learning module LM 14.5:
Differentiability and the chain rule:
Learning module LM 14.6:
Gradients and directional derivatives:
Learning module LM 14.7:
Local maxima and minima:
Learning module LM 14.8:
Absolute maxima and Lagrange multipliers:
Absolute maxima
Lagrange multipliers I
Lagrange multipliers II
Chapter 15: Multiple Integrals
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Absolute maxima
Absolute Maxima
We now consider Absolute Extrema problems similar to ones you met in
single variable calculus except that now $z = f(x,\, y)$ is optimized
over a region $D$ in the $xy$-plane, not just an interval $[a,\, b]$
on the $x$-axis. For simplicity, we'll assume that the region $D$ lies
inside a disk and has a boundary; typical examples are the
green-shaded regions in
where the boundary is shown in darkgreen.
Definition: If $(a, \,b)$ is a point in a region $D$ in the $xy$-plane,
then $z=f(x, \,y)$ is said to have
an Absolute Maximum on $D$ at $(a,\,b)$ when $f(x, \,y) \le f(a, \,b)$ holds
for all $(x,\, y)$ in $D$,
an Absolute Minimum on $D$ at $(a,\,b)$ when $f(x, \,y) \ge f(a, \,b)$
holds for all $(x,\, y)$ in $D$. Absolute maxima and minima are also
called Global maxima and minima.
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Just as with functions of one variable,
when $f(x,\,y)$ is continuous on $D$ an absolute maximum and
an absolute minimum always occur somewhere on
$D$. Locating them uses basically the same method as in one variable:
Theorem: An absolute maximum (resp. absolute minimum)
of $z = f(x,\,y)$ on $D$ occurs at a critical point inside
$D$ or at a point on the Boundary of $D$.
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The reason is the same as in one dimension. If you have a point in
the interior where $\nabla f \ne 0$, then you can get to a larger value
of $f$ by moving in the $\nabla f$ direction, and get to a smaller value by
moving in the $-\nabla f$ direction. If you keep moving in the $\nabla f$
direction, you will either approach a critical point, or you will hit
the boundary.
Our strategy to find absolute maxima and minima is
- Find the critical points on the interior. This involves computing
$\nabla f(x,\,y)$ and setting it equal to zero.
- Maximize (or minimize)
$f(x,\,y)$ on the boundary.
- Compare the values of $f(x,\,y)$ that you found from the first two steps.
Whichever is biggest is the absolute maximum.
- Likewise, whichever is smallest is the absolute minimum.
Example 1: Determine the absolute minimum value of
$$f(x,\, y) \ = \ x + 3y - 4xy$$
on the unit square shown in
Solution: First we find the critical points of
$$f(x,\, y) \ = \ x + 3y - 4xy\,.$$
Now
$$\frac{\partial f}{\partial x} \,=\,1 - 4y \,=\, 0,\quad \frac{\partial f}{\partial y} \,=\,3 - 4x\,=\,0\,,$$
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so the only critical point is $\bigl(\frac{3}{4},\, \frac{1}{4}\bigl)$,
and this lies inside the unit square. At this critical point,
$$f\Bigl(\frac{3}{4}\,, \ \frac{1}{4}\Bigr) \ = \ \frac{3}{4}\,.$$
Next we look at the behavior of $f$ on the four edges separately, since the
minimum value of $f$ on the boundary must be the minimum value on one
of the edges.:
Edge 1: here $y = 0,\, 0\le x \le 1$, and
$$\min_{0\le x\le 1}\, f(x,\,0) \ = \ \min_{0\le x\le 1}\, x \ = \ 0\,.$$
Edge 2: here $x = 0,\, 0\le y \le 1$, and
$$\min_{0\le y\le 1}\, f(0,\,y) \ = \ \min_{0\le y\le 1}\, 3y \ = \ 0\,.$$
Edge 3: here $y = 1,\, 0\le x \le 1$, and
$$\min_{0\le x\le 1}\, f(x,\,1) \ = \ \min_{0\le x\le 1}\, (3-3x) \ = \ 0\,.$$
Edge 4: here $x = 1,\, 0\le y \le 1$, and
$$\min_{0\le y\le 1}\, f(1,\,y) \ = \ \min_{0\le y\le 1}\, (1-y) \ = \ 0\,.$$
Consequently, on the unit square $f$ has
$$\hbox{Abs Min Value} \ = \ \min \Bigl\{\, \frac{3}{4},\ 0,\ 0, \ 0,\ 0\, \Bigl\} \ = \ 0 \,.$$
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In Example 1 the extrema on the boundary could be determined by
straightforward inspection. In other cases we need to use the
max/min methods we learned for functions of one variable.
Example 2: Find the absolute maximum and minimum
values of
$$f(x,\, y) \ = \ x^2 + y^2 - x - y +1$$
on the unit disk
$$D \ = \ \big\{(x,\,y): x^2 + y^2 \le 1\big\}\,.
$$
Solution: The absolute maximum and minimum values occur either
at a critical point inside the disk, i.e., in the region $\{
(x,\,y) : x^2 + y^2 < 1\}$, or on the boundary of the disk,
i.e., on the circle $\{ (x,\,y) : x^2 + y^2 = 1\}$.
We first find the critical points: when
$$f(x,\, y) \ = \ x^2 + y^2 - x - y +1\,,$$
then
$$\frac{\partial f}{\partial x} \,=\,2x -1\,,\quad \frac{\partial f}{\partial y} \,=\,2y - 1\,=\,0\,.$$
So $(1/2,\,1/2)$ is the only critical point, and this lies inside the disk.
At this critical point
$$f\Big(\frac{1}{2},\ \frac{1}{2}\Big) \ = \ \frac{1}{2}\,.$$
We next look at the behavior of $f$ on the boundary.
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Since this is the circle
$${\bf r}(t) \ = \ \cos t \, {\bf i} + \sin t \, {\bf j} \,, \quad 0 \le t \le 2\pi\,,$$
the restriction of $f$ to the boundary is the function
$$f({\bf r} (t)) \ = \ 1 - \cos t - \sin t + 1\qquad \qquad$$
$$\qquad = \ 2 -\cos t - \sin t\,.$$
Finding the absolute extrema of $f({\bf r} (t))$ on $[0,\,2\pi]$ is a
single variable problem: the absolute extrema will occur either at a
critical point in $(0,\,2\pi)$, or at the endpoints $t=0,\,2\pi$. But
$$\frac{d}{dt}\,f({\bf r} (t)) \ = \ \sin t - \cos t\,,$$
so the critical points of $f({\bf r} (t))$ on $(0,\,2\pi)$ occur at $t
= \pi/4,\, 5\pi/4$. But
$$f\Big({\bf r} \Big(\frac{\pi}{4}\Big) \Big)= 2 - \sqrt{2}, \ f\Big({\bf r} \Big(\frac{5\pi}{4}\Big) \Big)
= 2+ \sqrt{2}\,,$$
while
$$f({\bf r} (0)) \ = \ 1,\quad f({\bf r} (2\pi)) \ = \ 1\,.$$
Thus
$$\max_D\, f(x,\,y) = 2+ \sqrt{2}\,, \ \ \min_D\,f(x,\,y)=\frac{1}{2}\,.$$
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