2: Partial Derivatives

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In this chapter we are going to generalize the definition of “derivative” to functions of more than one variable and then we are going to use those derivatives. We will parallel the development in Chapters 1 and 2 of the CLP-1 text. We shall

define limits and continuity of functions of more than one variable (Definitions 2.1.2 and 2.1.3) and then
study the properties of limits in more than one dimension (Theorem 2.1.5) and then
define derivatives of functions of more than one variable (Definition 2.2.1).

We are going to be able to speed things up considerably by recycling what we have already learned in the CLP-1 text.

We start by generalizing the definition of “limit” to functions of more than one variable.

2.1: Limits
Before we really start, let's recall some useful notation.
2.2: Partial Derivatives
We are now ready to define derivatives of functions of more than one variable.
2.3: Higher Order Derivatives
You have already observed, in your first Calculus course, that if f(x) is a function of x, then its derivative is also a function of x, and can be differentiated to give the second order derivatives, which can in turn be differentiated yet again to give the third order derivative, f(3)(x),f^{(3)}(x)\text{,} and so on.
2.4: The Chain Rule
You already routinely use the one dimensional chain rule
2.5: Tangent Planes and Normal Lines
The tangent line to the curve \(y=f(x)\) at the point \(\big(x_0,f(x_0)\big)\) is the straight line that fits the curve best at that point.
2.6: Linear Approximations and Error
A frequently used, and effective, strategy for building an understanding of the behaviour of a complicated function near a point is to approximate it by a simple function. The following suite of such approximations is standard fare in Calculus I courses. See, for example, §3.4 in the CLP-1 text.
2.7: Directional Derivatives and the Gradient
The principal interpretation of \(\frac{\mathrm{d}f}{\mathrm{d}x}(a)\) is the rate of change of \(f(x)\text{,}\) per unit change of \(x\text{,}\) at \(x=a\text{.}\) The natural analog of this interpretation for multivariable functions is the directional derivative, which we now introduce through a question.
2.8: Optional — Solving the Wave Equation
Many phenomena are modelled by equations that relate the rates of change of various quantities. As rates of change are given by derivatives the resulting equations contain derivatives and so are called differential equations.
2.9: Maximum and Minimum Values
One of the core topics in single variable calculus courses is finding the maxima and minima of functions of one variable. We'll now extend that discussion to functions of more than one variable.
2.10: Lagrange Multipliers
In the last section we had to solve a number of problems of the form “What is the maximum value of the function \(f\) on the curve \(C\text{?}\)” In those examples, the curve \(C\) was simple enough that we could reduce the problem to finding the maximum of a function of one variable.