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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



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.

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.

The reason is the same as in one dimension. If you have a point in the interior where f0, 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

  1. Find the critical points on the interior. This involves computing f(x,y) and setting it equal to zero.
  2. Maximize (or minimize) f(x,y) on the boundary.
  3. Compare the values of f(x,y) that you found from the first two steps. Whichever is biggest is the absolute maximum.
  4. Likewise, whichever is smallest is the absolute minimum.

    Example 1: Determine the absolute minimum value of f(x,y) = x+3y4xy on the unit square shown in

Solution: First we find the critical points of f(x,y) = x+3y4xy. Now fx=14y=0,fy=34x=0,
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=0,0x1, 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 \,.


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.
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}\,.