# Conservative Vector Fields

Recall from the Vector Fields page that if $z = f(x, y)$ is a two variable real-valued function, then the gradient of $f$, $\nabla f(x, y) = \frac{\partial f}{\partial x} \vec{i} + \frac{\partial f}{\partial y} \vec{j}$ defines a vector field on $\mathbb{R}^2$. Similarly, for a three variable real-valued function $w = f(x, y, z)$, the gradient of $f$, $\nabla f(x, y, z) = \frac{\partial f}{\partial x} \vec{i} + \frac{\partial f}{\partial y} \vec{j} + \frac{\partial f}{\partial z} \vec{k}$ defines a vector field on $\mathbb{R}^3$.

Now we have already seen that the gradient of a scalar field produces a vector field, however, there exists vector fields in which the gradient of any scalar field does not equal this vector field.

Definition: Let $\mathbf{F} = \mathbf{F}(x, y)$ be a vector field on $\mathbb{R}^2$. $\mathbf{F}$ is said to be a Conservative vector field on the domain $D$ if there exists a function $\phi$ such that the gradient of $\phi$ equals $\mathbf{F}$, that is $\mathbf{F} (x, y) = \nabla \phi (x, y)$. The function $\phi$ is called a Potential for $\mathbf{F}$ on $D$. |

*Conservative vector fields can be defined on higher dimensions as well in an analogous manner. Furthermore, we note that the potential cannot have any singular points in $D$ since the first partial derivatives of $\phi$ must exist for every point in $D$ to ensure that $\mathbf{F}(x, y) = \nabla \phi (x, y)$ for all points in $D$.*

Now the following theorem will give us necessary conditions to ensure that a vector field on $\mathbb{R}^2$ is conservative.

Theorem 1: If the vector field $\mathbf{F}(x, y) = P(x, y) \vec{i} + Q(x, y) \vec{j}$ on $\mathbb{R}^2$ is conservative on the domain $D$, then $\frac{\partial P}{\partial y} = \frac{\partial Q}{\partial x}$ for all points $(x, y) \in D$. |

**Proof:**Suppose that $\mathbf{F}(x, y) = P(x, y) \vec{i} + Q(x, y) \vec{j}$ is a conservative vector field on $\mathbb{R}^2$. Then there exist a potential $\phi (x, y)$ such that:

(1)

- The equation above implies that:

(2)

- For the lefthand equation, take the partial derivatives of both sides with respect to $y$. For the righthand equation, take the partial derivatives of both sides with respect to $x$. We thus get the following equations:

(3)

- The mixed partial derivatives $\frac{\partial^2 \phi}{\partial y \partial x} = \frac{\partial^2 \phi}{\partial x \partial y}$ by Clairaut’s Theorem on Higher Order Partial Derivatives and thus:

(4)

Theorem 2: If the vector field $\mathbf{F}(x, y, z) = P(x, y, z) \vec{i} + Q(x, y, z) \vec{j} + R(x, y, z) \vec{k}$ on $\mathbb{R}^3$ is conservative on the domain $D$, then $\frac{\partial P}{\partial y} = \frac{\partial Q}{\partial x}$, $\frac{\partial P}{\partial z} = \frac{\partial R}{\partial x}$, and $\frac{\partial Q}{\partial z} = \frac{\partial R}{\partial y}$ for all points $(x, y, z) \in D$. |

**Proof:**Suppose that $\mathbf{F}(x, y, z) = P(x, y, z) \vec{i} + Q(x, y, z) \vec{j} + R(x, y, z) \vec{k}$ is a conservative vector field on $\mathbb{R}^3$. Then there exists a potential $\phi(x, y, z)$ such that:

(5)

- The equation above implies that:

(6)

- If we partial differentiate the first equation with respect to $y$ and the second equation with respect to $x$, then we have that $\frac{\partial P}{\partial y} = \frac{\partial^2 \phi}{\partial y \partial x} = \frac{\partial ^2 \phi}{\partial x \partial y} = \frac{\partial Q}{\partial x}$.

- If we partial differentiate the first equation with respect to $z$ and the third equation with respect to $z$, then we have that $\frac{\partial P}{\partial z} = \frac{\partial^2 \phi}{\partial z \partial x} = \frac{\partial^2 \phi}{\partial x \partial z} = \frac{\partial R}{\partial x}$.

- If we partial differentiate the second equation with respect to $z$ and the third equation with respect to $y$, then we have that $\frac{\partial Q}{\partial z} = \frac{\partial^2 \phi}{\partial z \partial y} = \frac{\partial^2 \phi}{\partial y \partial z} = \frac{\partial R}{\partial y}$.

- Thus Theorem 2 holds. $\blacksquare$

Remark 1: Theorem 1 and Theorem 2 above are NOT sufficient to guarantee that a vector field $\mathbf{F}$ on $\mathbb{R}^2$ or $\mathbb{R}^3$ is conservative, that is, the converses of these theorems are not necessarily true. |

Let’s look at an example of showing that a vector field is conservative.

## Example 1

**Determine whether the vector field $\mathbf{F}(x, y, z) = x \vec{i} – 2y \vec{j} + 3z \vec{k}$ is conservative or not.**

We will first see if we can immediately determine if $\mathbf{F}$ is not conservative by seeing whether or not Theorem 2 holds. We calculate the necessary partial derivatives as follows:

(7)

So we suspect that $\mathbf{F}$ may be a conservative vector field. If so, then we have that for some functions $a, b, m, n, p,$ and $q$:

(8)

Thus we have that:

(9)

The equation above implies that $\frac{x^2}{2} = m(x) = p(x)$, $-y^2 = a(y) = q(y)$ and $\frac{3z^2}{2} = b(z) = n(z)$. For any constant $C$, a potential for $\mathbf{F}$ is:

(10)

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