## Description

example

g = gradient(f,v) finds the gradient vector of the scalar function f with respect to vector v in Cartesian coordinates. The input f is a function of symbolic scalar variables and the vector v specifies the scalar differentiation variables.

g = gradient(f) finds the gradient vector of the scalar function f with respect to a vector constructed from all symbolic scalar variables found in f. The order of variables in this vector is defined by symvar.

gM = gradient(fM,vM) finds the gradient vector of the scalar function fM with respect to vector vM in Cartesian coordinates. The input function fM is a function of symbolic matrix variables and the vector vM is a symbolic matrix variable of size 1-by-N or N-by-1.

## Examples

collapse all

The gradient of a scalar 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(x,y,z) = 2*y*z*sin(x) + 3*x*sin(z)*cos(y);
ans(x, y, z) =

$\left(\begin{array}{c}3 \mathrm{cos}\left(y\right) \mathrm{sin}\left(z\right)+2 y z \mathrm{cos}\left(x\right)\\ 2 z \mathrm{sin}\left(x\right)-3 x \mathrm{sin}\left(y\right) \mathrm{sin}\left(z\right)\\ 2 y \mathrm{sin}\left(x\right)+3 x \mathrm{cos}\left(y\right) \mathrm{cos}\left(z\right)\end{array}\right)$

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 =

$\left(\begin{array}{c}-2 \mathrm{cos}\left(x\right) \left(\mathrm{sin}\left(x\right)+\mathrm{sin}\left(y\right)\right)\\ -2 \mathrm{cos}\left(y\right) \left(\mathrm{sin}\left(x\right)+\mathrm{sin}\left(y\right)\right)\end{array}\right)$

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)

Use symbolic matrix variables to define a matrix multiplication that returns a scalar.

syms X Y [3 1] matrix
A = Y.'*X
A = ${Y}^{\mathrm{T}} X$

Find the gradient of the matrix multiplication with respect to $X$.

gX = $Y$

Find the gradient of the matrix multiplication with respect to $Y$.

gY = $X$

Find the gradient of the multivariable function

$f\left(x\right)={\mathrm{sin}}^{2}\left({x}_{1,1}\right)+{\mathrm{sin}}^{2}\left({x}_{1,2}\right)+{\mathrm{sin}}^{2}\left({x}_{1,3}\right)$

with respect to the vector $x=\left[{x}_{1,1},{x}_{1,2},{x}_{1,3}\right]$.

Use symbolic matrix variable to express the function $f$ and its gradient in terms of the vector $x$.

syms x [1 3] matrix
f = sin(x)*sin(x).'
f = $\mathrm{sin}\left(x\right) {\mathrm{sin}\left(x\right)}^{\mathrm{T}}$
g = $2 \left(\mathrm{cos}\left(x\right)\odot {\mathrm{I}}_{3}\right) {\mathrm{sin}\left(x\right)}^{\mathrm{T}}$

To show the gradient in terms of the elements of $x$, convert the result to a vector of symbolic scalar variables using symmatrix2sym.

g = symmatrix2sym(g)
g =

$\left(\begin{array}{c}2 \mathrm{cos}\left({x}_{1,1}\right) \mathrm{sin}\left({x}_{1,1}\right)\\ 2 \mathrm{cos}\left({x}_{1,2}\right) \mathrm{sin}\left({x}_{1,2}\right)\\ 2 \mathrm{cos}\left({x}_{1,3}\right) \mathrm{sin}\left({x}_{1,3}\right)\end{array}\right)$

Alternatively, you can convert $f$ and $x$ to symbolic expressions of scalar variables and use them as inputs to the gradient function.

g =

$\left(\begin{array}{c}2 \mathrm{cos}\left({x}_{1,1}\right) \mathrm{sin}\left({x}_{1,1}\right)\\ 2 \mathrm{cos}\left({x}_{1,2}\right) \mathrm{sin}\left({x}_{1,2}\right)\\ 2 \mathrm{cos}\left({x}_{1,3}\right) \mathrm{sin}\left({x}_{1,3}\right)\end{array}\right)$

## Input Arguments

collapse all

Scalar function, specified as a symbolic expression or symbolic function that is a function of symbolic scalar variables.

Data Types: sym | symfun

Vector with respect to which you find gradient vector, specified as a symbolic vector. By default, v is a vector constructed from all symbolic scalar variables found in f. The order of variables in this vector is defined by symvar.

If v is a scalar, gradient(f,v) = diff(f,v). If v is an empty symbolic object, such as sym([]), then gradient returns an empty symbolic object.

Data Types: sym

Scalar function, specified as a symbolic expression that is a function of symbolic matrix variables.

Data Types: symmatrix

Vector with respect to which you find gradient vector, specified as a symbolic matrix variable of size 1-by-N or N-by-1.

Data Types: symmatrix

## Output Arguments

collapse all

Gradient vector, returned as a symbolic expression or symbolic function that is a function of symbolic scalar variables.

Data Types: sym | symfun

Gradient vector, returned as a symbolic expression that is a function of symbolic matrix variables.

Data Types: symmatrix

collapse all

The gradient vector of f(x) with respect to the vector $x=\left({x}_{1},{x}_{2},\dots ,{x}_{n}\right)$ is the vector of the first partial derivatives of f.
${\nabla }_{x}f\left(x\right)=\left(\frac{\partial f}{\partial {x}_{1}},\frac{\partial f}{\partial {x}_{2}},\dots ,\frac{\partial f}{\partial {x}_{n}}\right)$