M408M Learning Module Pages
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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)≤f(a,b) holds
for all (x,y) in D,
an Absolute Minimum on D at (a,b) when f(x,y)≥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 ∇f≠0, then you can get to a larger value
of f by moving in the ∇f direction, and get to a smaller value by
moving in the −∇f direction. If you keep moving in the ∇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
∇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
∂f∂x=1−4y=0,∂f∂y=3−4x=0,
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so the only critical point is (34,14),
and this lies inside the unit square. At this critical point,
f(34, 14) = 34.
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≤x≤1, and
min
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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