In Lecture 3 we finished up the topic of line integrals. In this lecture we move on to the subject of the Gradient function, and how it relates to surfaces in space.
The gradient of a function is a Vector defined as:

f (dot)
u. The derivative of f in the u direction is defined to be
the limit below:

f(x,y,z) is the
direction of "steepest ascent" of the surface.
f (dot)
u, you can see that this is the case when
f is in the same direction as u. Thus, the direction
of most severe change in f is the direction of
f.

g will point in the direction in the (x,y) plane you would walk
to go up the steepest part of the hill, and ||
g|| would be the slope of the hill.Level Curves: Level Curves are curves in the x-y plane that trace out g(x,y)=C, where C is a constant. That is, curves which yield a constant height on the hill created by z=g(x,y).
g points perpendicular to the level curves.
This can be proven by thinking about the first use of gradients again.
g (dot) u
g (dot) T, a tangent to a level curve, is the change
of g when one moves in the T direction. Since T is along a
line of g(x,y) = A constant, there is no change. So
g (dot) T = 0
g must be perpendicular to T, and
therefore to the level curve.
f is perpindicular to the surface.
f (dot) T = 0.
f is perpendicular to the surface.
f / ||
f||
f,
g, observe the relationship between them, and find the
derivitive of f in the x direction.
g = 2xi + 2yj =
2(xi + yj)
f = 2xi + 2yj - k
Note that
g is the projection of
f onto the x-y plane.
To find the derivative of f in the x (i) direction:
f (dot) i = 2x