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SymbolicTracingUtils.jl

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SymbolicTracingUtils is a lightweight abstraction layer designed to simplify switching between symbolic tracing packages like Symbolics.jl and FastDifferentiation.jl.

Unlike other abstraction layers such as DifferentiationInterface.jl, this package is not limited to automatic differentiation use cases but also supports scenarios where symbolic tracing is used to generate efficient implementations of user-defined functions (irrespective of derivative computation).

Features

  • Seamless integration with both Symbolics.jl and FastDifferentiation.jl.
  • Consistent APIs for creating symbolic variables, building callable functions, and computing gradients or Jacobians.
  • Support for both dense and sparse Jacobian computation.
  • Lightweight and minimalistic design.

Installation

To install SymbolicTracingUtils, use the Julia package manager:

using Pkg
Pkg.add("SymbolicTracingUtils")

Usage

Creating Symbolic Variables

using SymbolicTracingUtils
backend = SymbolicsBackend()
x = make_variables(backend, :x, 3)  # Creates a 3-element vector of symbolic variables `x[1]`, `x[2]`, `x[3]`

Building Callable Functions

f_symbolic = x[1]^2 + x[2]*x[3]
f_callable = build_function(f_symbolic, x; in_place = false)
result = f_callable([1.0, 2.0, 3.0])  # Evaluate the function

Computing Gradients

grad_symbolic = gradient(f_symbolic, x)
grad_callable = build_function(grad_symbolic, x; in_place = false)
grad_callable([1.0, 2.0, 3.0])

Computing Jacobians

f_vector = [x[1]^2, x[2]*x[3]]
jac_symbolic = jacobian(f_vector, x)  # Dense Jacobian
sparse_jac_symbolic = sparse_jacobian(f_vector, x)  # Sparse Jacobian

License

This project is licensed under the MIT License.

Contributions, bug reports, and feature requests are welcome!