# Difference between revisions of "Gradients and Directional Derivatives"

Field: Calculus
Image Created By: Golden Software
Website: [1]

This image shows gradient vectors at different points on a contour map. These vectors show the paths of steepest descent at different points on the landscape.

# Basic Description

Graph and contour map of a function: Click to enlarge

A contour map utilizes the concept of level sets. A level set is the set of all points generated when a function is made equal to a constant. For example, one level set of the function $z = x^2 -y^2$ is $3=x^2-y^2$. Setting a 3-d function equal to a constant in this way yields a contour curve. These curves are curves with constant z-component, and when representing a map, they show constant height.

A contour map is simply a collection of contour curves, each with the given function set equal to a different constant, meaning each curve represents a different constant height.

Now suppose instead of seeking points of constant height, we wish to find points along which height changes most rapidly. Intuitively, we travel perpendicular to contour curves, since even partially traveling along contour curves would involve traveling along a level set. This page's main image shows a number of vectors perpendicular to contours, meaning they represent the most rapid change of height (in this case, in the descending direction) from a point.

# A More Mathematical Explanation

As mentioned above, the general strategy for finding a level set is setting a function equal to a con [...]

As mentioned above, the general strategy for finding a level set is setting a function equal to a constant. The gradient is a useful idea. For a function F, the gradient is defined as $\nabla f = (\partial{f}/\partial{x} , \partial{f}/\partial{y} ,\partial{f}/\partial{z})$

# Teaching Materials

  [[Description::This image shows gradient vectors at different points on a contour map.  These vectors show the paths of steepest descent at different points on the landscape.|]]