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## Multivariable calculus

### Course: Multivariable calculus > Unit 4

Lesson 4: Line integrals in vector fields (articles)# Fundamental theorem of line integrals

Also known as the Gradient Theorem, this generalizes the fundamental theorem of calculus to line integrals through a vector field.

## Background

Only needed if you want to understand the proof:

## What we're building to

- The fundamental theorem of line integrals, also called the gradient theorem, states that
- The intuition behind this formula is that each side represents the change in the value of a multivariable function start color #0c7f99, f, end color #0c7f99 as you walk along a path parameterized by start color #0d923f, start bold text, r, end bold text, with, vector, on top, left parenthesis, t, right parenthesis, end color #0d923f.
- This formula implies that gradient fields are
**path independent**, meaning the line integrals along any two paths connecting the same start and end points will be equal.

## Statement of the theorem

Recall that the fundamental theorem of calculus in the single-variable world states that

In some sense, this says that integration is the opposite of differentiation.

The fundamental theorem of line integrals, also known as the gradient theorem, is one of several ways to extend this theorem into higher dimensions. In a sense, it says that line integration through a vector field is the opposite of the gradient. The statement of the theorem is that

Where

- start color #0c7f99, f, end color #0c7f99 is some scalar-valued multivariable function.
- del, start color #0c7f99, f, end color #0c7f99 is the gradient of start color #0c7f99, f, end color #0c7f99.
- start color #0d923f, start bold text, r, end bold text, with, vector, on top, end color #0d923f, left parenthesis, t, right parenthesis is a vector-valued function which parameterizes some path through the input space of start color #0c7f99, f, end color #0c7f99.
- start bold text, r, end bold text, with, vector, on top, left parenthesis, a, right parenthesis and start bold text, r, end bold text, with, vector, on top, left parenthesis, b, right parenthesis are the end points of the path.
- start color #bc2612, start bold text, r, end bold text, with, vector, on top, prime, end color #bc2612, left parenthesis, t, right parenthesis is the derivative of start color #0d923f, start bold text, r, end bold text, with, vector, on top, end color #0d923f, left parenthesis, t, right parenthesis, taken component-wise as usual.

You might also see this theorem written without reference to the parameterization start bold text, r, end bold text, with, vector, on top, left parenthesis, t, right parenthesis as follows:

Where C represents the path through space, with A as its starting point and B as its ending point, and d, start bold text, s, end bold text is thought of as a tiny step along C.

In short, the theorem states that the line integral of the gradient of a function f gives the total change in the value of f from the start of the curve to its end.

## The intuition

The meaning behind this formula is actually fairly straightforward, once we take some time to digest the meaning of each term. There are two main players on the stage right now:

- A path wandering through space (let's say two-dimensional space, for now, to make drawing easier).
- A function f which takes in points of that path as its input, and outputs a number.

Think about how the value of the function f changes as we walk along the path. The following video shows one way to visualize this, where the graph of some function f is shown in blue, a path in the x, y-plane is shown in red, and the projection of that path onto the graph is also shown in red.

Think about the height of this graph above each point on the path. How could you mathematically keep track of the change in this height as we walk along the path.

Instead of projecting the path onto the graph of f, we could also overlay it with the gradient field of f (the vector field where each vector represents del, f):

Let's write down the gradient theorem again:

The next few questions will have you reason through the left-hand side of this expression.

**Concept check 1**: If we think of d, t as a very slight change to the parameter t, how can you interpret the vector start color #bc2612, start bold text, r, end bold text, with, vector, on top, prime, left parenthesis, t, right parenthesis, d, t, end color #bc2612?

**Concept check 2**: How can you interpret the dot product

where P is some point in space and start bold text, v, end bold text, with, vector, on top is some vector?

**Concept check 3**: Given these two facts, how can we interpret the dot product start color #0c7f99, del, f, left parenthesis, start bold text, r, end bold text, with, vector, on top, left parenthesis, t, right parenthesis, right parenthesis, end color #0c7f99, dot, start color #bc2612, start bold text, r, end bold text, with, vector, on top, prime, left parenthesis, t, right parenthesis, end color #bc2612?

**Concept check 4**: Finally, how can you interpret the integral

However, there is a much simpler way to think about the change in the value of f from the start of the path to its end: Just evaluate f at both ends, and subtract the difference:

In other words, each side of the equation in the gradient theorem computes the change in f across the path, but the left side thinks of it step-by-step, while the right side takes a global perspective.

## A quick proof

Using the multivariable chain rule, we have

Plugging this into the statement of the gradient theorem, we see it becomes the same as the fundamental theorem of calculus

Tada!

This proof leverages the powerful fundamental theorem of calculus, along with the multivariable chain rule, and hence looks deceptively simple. A good exercise in understanding this theorem is to think through how exactly this quick and tidy three-line proof encapsulates the intuition for the gradient theorem spelled out in the last section.

There's nothing wrong with using other powerful theorems to help prove new results. In fact, to avoid doing so would be foolish. However, walking through such proofs is often not enough for a deeper understanding, so it's healthy to unravel new results into their full meaning, seeing how they stand up on their own.

## Example: A sinusoidal path

Define f as

Let C represent the path parameterized by

between the values t, equals, 0 and t, equals, 2, pi.

Compute the integral

### Solution 1: The old fashioned way

We can spell out the full line integral and compute it. In preparation, we will need to evaluate the gradient of f, left parenthesis, x, comma, y, right parenthesis, equals, x, squared, plus, y, squared.

What is del, f?

We will also need the derivative of $\vec{\textbf{r}}(t) = \left[\begin{array}{c} t \\ \sin(t) \end{array} \right]$.

What is start bold text, r, end bold text, with, vector, on top, prime, left parenthesis, t, right parenthesis?

Finally, what do you get when you plug these in and chug through the line integral?

### Solution 2: Apply the fundamental theorem of line integrals

Applying the fundamental theorem of line integrals, we can skip over many of the steps from the previous solution, including the computation of the gradient of f and the derivative of start bold text, r, end bold text, with, vector, on top, left parenthesis, t, right parenthesis.

Solve the line integral above using the gradient theorem.

If you look back through the full computation of the line integral in solution 1, the computations we performed actually feel pretty silly. We took the derivative of everything, including the partial derivatives of x, squared, plus, y, squared and the ordinary derivatives of t and sine, left parenthesis, t, right parenthesis, then later integrated those derivatives back up to where they started.

Working through this should also help build an intuition for how the fundamental theorem of line integrals derives from the fundamental theorem of calculus.

## Path independence

The gradient theorem has a really important consequence regarding gradient fields. Suppose you have two distinct curves C, start subscript, 1, end subscript and C, start subscript, 2, end subscript, each connecting the same two points A and B. Let's say these are wandering through the gradient field of some scalar-valued function f:

According to the gradient theorem, the line integral of del, f along each of these curves will be the same, since that integral is completely determined by the value of f at A and B:

We explore this idea further in the next article on conservative vector fields.

## Summary

- The fundamental theorem of line integrals, also called the gradient theorem, states that
- The intuition behind this formula is that each side represents the change in the value of a multivariable function start color #0c7f99, f, end color #0c7f99 as you walk along a path parameterized by start color #0d923f, start bold text, r, end bold text, with, vector, on top, left parenthesis, t, right parenthesis, end color #0d923f.
- This formula implies that gradient fields are
**path independent**, meaning the line integrals along any two paths connecting the same start and end points will be equal.

## Want to join the conversation?

- How come in these articles were taking the gradient of f along the vector field r, but in the videos sal is doing with work he is taking the integral of the function along the vector field r times dr?(2 votes)
- In the videos, Sal started with a vector-valued function,
**f**(x,y), and showed that it was the gradient of a scalar function, F(x,y). Then he showed that the value of the line integral of the dot product of**f**and d*r*, along some curve, C, was equivalent to the difference between F at the end point and start point, F(b)-F(a) (where F is written as a function of time, t).

In the article, we start with the scalar function F, then showed that the line integral of the dot product of ∇F and d*r* was F(b) - F(a). So both the article and the video show the same thing. The video starts with the gradient of F and works backwards to find F, while the article starts with F itself.(3 votes)

- The first line of the quick proof seems wrong. The dt on the right side of the equation is unnecessary. So does the dt in the next line.(1 vote)
**r**'(t) = d**r**/dt**r**'(t).dt = (d**r**/dt).dt = d**r**

Gotta integrate over some sorta imperceptible step (*imperceptible*from the perspective of the Reals that is!) So whether expressed as d**r**or d**r**in terms of dt, it all seems rather right and necessary to me...(4 votes)

- r⃗ ′(t) is the derivative of r(t) right?(2 votes)
- Yes, r⃗ '(t) (with the single quote) is the derivative of r(t), it's taken in each component in the vector separately...(2 votes)

- this is just u-substitution, anybody realize that?(2 votes)
- If the path loops around back to the same point, is the value of the integral 0?(1 vote)
- Why is del-F(p) dotted with vector-v a directional derivative? I thought for this formula to work for directional derivative, that the "v" must be a UNIT VECTOR. The author of the article does not say "v" is a unit vector. He specifically describes "v" as "some vector".(1 vote)
- * i think the statement- "
*The rate at which 'f' changes as you move away from 'P' with a velocity given by 'v'*" in ' concept check 2 ' is wrong because

*`even if we move with any velocity 'v', 'f' will always change in same manner as with any other 'v' with respect to 'x' & 'y'`

(1 vote)- But v is a vector in this case, not a scalar, so the direction of motion is important. We'll get different rates of change of f if we move in different directions.(1 vote)

- isn't where we see the term nabla f r(t) times r'(t) in the fundamental theorem of line integrals merely a consequence of the chain rule?(1 vote)