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This article/section deals with mathematical concepts appropriate for late high school or early college. |
In mathematics, the gradient is a vector associated to a point of a differentiable function
which takes real values. Specifically, the gradient at
is a vector in
which points in the direction in which
increases most rapidly at
. The magnitude of the gradient at
is equal to the maximum directional derivative of
at
. The gradient is an extension of the idea of derivative to functions with more than one variable.
Stated another way, a gradient is a vector that has coordinate components that consist of the partial derivatives of a function with respect to each of its variables. For example, if , then
Observe that in this case, the gradient vector is orthogonal to the "level curve" defined by
, which here is a circle: the gradient points outward from the origin, which is the direction of steepest increase of
, and vectors outward from the origin are perpendicular to circles centered at the origin. We'll see later that this is a case of a more general property of the gradient.
More precisely, we define the gradient, of
to be the vector field:
consisting of the various partial derivatives of . If
is a unit vector in
, then, by the chain rule, the directional derivative of
in the direction of
is simply the dot product:
Evidently by the Cauchy-Schwartz inequality, the directional derivative in the direction is maximal in the direction of the gradient, and equal to
for
a unit vector in the direction of the gradient.
If is a differentiable function with smooth level sets
, then the gradient vector field
is perpendicular to the level sets of
. For fix a level set
, and let
be a vector tangent to
at
. Then we can find a curve
on
with
. Now
since is a level set. Taking derivatives of both sides and applying the chain rule, we get that
Thus, is perpendicular to
at
, i.e., the gradient of
is perpendicular to the level sets of
.
Categories: [Calculus] [Vector Analysis]