Gradient vector of scalar function

- example
`gradient(f,v)`

`gradient(`

finds
the gradient vector of the scalar function `f`

,`v`

)`f`

with
respect to vector `v`

in Cartesian coordinates.

If you do not specify `v`

, then `gradient(f)`

finds
the gradient vector of the scalar function `f`

with
respect to a vector constructed from all symbolic variables found
in `f`

. The order of variables in this vector is
defined by `symvar`

.

The gradient of a function `f`

with
respect to the vector `v`

is the vector of the first
partial derivatives of `f`

with respect to each element
of `v`

.

Find the gradient vector of `f(x, y, z)`

with
respect to vector `[x, y, z]`

. The gradient is a
vector with these components.

syms x y z f = 2*y*z*sin(x) + 3*x*sin(z)*cos(y); gradient(f, [x, y, z])

ans = 3*cos(y)*sin(z) + 2*y*z*cos(x) 2*z*sin(x) - 3*x*sin(y)*sin(z) 2*y*sin(x) + 3*x*cos(y)*cos(z)

Find the gradient of a function ```
f(x,
y)
```

, and plot it as a quiver (velocity) plot.

Find the gradient vector of `f(x, y)`

with
respect to vector `[x, y]`

. The gradient is vector `g`

with
these components.

syms x y f = -(sin(x) + sin(y))^2; g = gradient(f, [x, y])

g = -2*cos(x)*(sin(x) + sin(y)) -2*cos(y)*(sin(x) + sin(y))

Now plot the vector field defined by these components. MATLAB^{®} provides
the `quiver`

plotting function
for this task. The function does not accept symbolic arguments. First,
replace symbolic variables in expressions for components of `g`

with
numeric values. Then use `quiver`

:

[X, Y] = meshgrid(-1:.1:1,-1:.1:1); G1 = subs(g(1), [x y], {X,Y}); G2 = subs(g(2), [x y], {X,Y}); quiver(X, Y, G1, G2)

`curl`

| `diff`

| `divergence`

| `hessian`

| `jacobian`

| `laplacian`

| `potential`

| `quiver`

| `vectorPotential`

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