# Problem with Symbolic package: computation of symbolic gradient

**URL:** <https://discourse.julialang.org/t/problem-with-symbolic-package-computation-of-symbolic-gradient/87882>\
**Category:** New to Julia\
**Tags:** question\
**Created:** [September 27, 2022, 6:01pm UTC](https://discourse.julialang.org/t/problem-with-symbolic-package-computation-of-symbolic-gradient/87882 "2022-09-27T18:01:10Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![Luca](https://avatars.discourse-cdn.com/v4/letter/l/51bf81/32.png) [@Luca](https://discourse.julialang.org/u/Luca)\
**Post date:** [September 27, 2022, 6:01pm UTC](https://discourse.julialang.org/t/problem-with-symbolic-package-computation-of-symbolic-gradient/87882/1 "2022-09-27T18:01:10Z")

</div>

Dear all,

I’m trying to use Symbolics package in order to implement the symbolic computation of the gradient of a function. For instance given `sin(8.0*x1*x2)`, I want to compute the partial derivative w.r.t. `x1`. I implemented the following code:

```julia
using Symbolics
@variables x1, x2
f = sin(8.0 * x1 * x2)
D = Differential(x1)
println(eval(build_function(expand_derivatives(D(f)),x1,x2)))(pi,1/8.0)

```

However I obtain the error

```julia
ERROR: LoadError: MethodError: no method matching (::var"#15#16")(::Vector{Float64})
Closest candidates are:
  (::var"#15#16")(::Any, ::Any) at /Users/mencarelli/.julia/packages/SymbolicUtils/qulQp/src/code.jl:349 (method too new to be called from this world context.)

```

Secondly, I need to implement a loop to compute all the partial derivatives for a function with 20 variables and store them in a variable `gradient`. How can I do? Thanks for all the suggestions.

---

<div class="post-metadata">

**Author:** ![qwerty](https://avatars.discourse-cdn.com/v4/letter/q/4491bb/32.png) [@qwerty](https://discourse.julialang.org/u/qwerty)\
**Post date:** [September 27, 2022, 8:10pm UTC](https://discourse.julialang.org/t/problem-with-symbolic-package-computation-of-symbolic-gradient/87882/2 "2022-09-27T20:10:45Z")

</div>

```julia
@variables x1 x2
f = sin(8.0 * x1 * x2)
D = Differential(x1)

your_function = expand_derivatives(D(f))
# y_f = Symbolics.derivative(f, x1) # alternative

# evaluate the expression at x1=π, x2=1/8
substitute(your_function, Dict(x1=>π, x2=>1/8))

# if you want a callable julia function try this
callable_your_function = eval(build_function(your_function, x1, x2))
callable_your_function(π, 1/8)

# last question
your_gradient = Symbolics.gradient(f, Symbolics.get_variables(f))

```