# Chapter 13: Functions of Multiple Variables and Partial Derivatives

- Page ID
- 25081

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- 13.1: Functions of Multiple Variables
- Our first step is to explain what a function of more than one variable is, starting with functions of two independent variables. This step includes identifying the domain and range of such functions and learning how to graph them. We also examine ways to relate the graphs of functions in three dimensions to graphs of more familiar planar functions.

- 13.6: Directional Derivatives and the Gradient
- A function z = f(x,y) has two partial derivatives: ∂z/∂x and ∂z/∂y. These derivatives correspond to each of the independent variables and can be interpreted as instantaneous rates of change (that is, as slopes of a tangent line) parallel to one of the axes. In this section we consider derivatives in more general directions.

- 13.7: Minima/Maxima Problems
- One of the most useful applications for derivatives of a function of one variable is determining maximum and/or minimum values. This application is also important for functions of two or more variables, but as we have seen in earlier sections of this chapter, the introduction of more independent variables leads to more possible outcomes for the calculations. Thus, in this section we adapt the ideas of critical points and derivative tests to multivariable functions.

- 13.8: Lagrange Multipliers
- Solving optimization problems for functions of two or more variables can be similar to solving such problems in single-variable calculus. However, techniques for dealing with multiple variables allow us to solve more varied optimization problems for which we need to deal with additional conditions or constraints. In this section, we examine one of the more common and useful methods for solving optimization problems with constraints.

- 13.E: Chapter 13 Review Exercises
- Exercises for self-study or review of Chapter 13.