When working with Julia, it is common to encounter situations where you need to use external libraries or packages to solve specific problems. One such library is CasADi, which is a powerful tool for numerical optimization and automatic differentiation. In this article, we will explore different ways to use CasADi from Julia and discuss the pros and cons of each approach.
Option 1: Using the CasADi Julia Package
The easiest way to use CasADi from Julia is by installing the CasADi Julia package. This package provides a Julia interface to the CasADi library, allowing you to use CasADi functions and features directly in your Julia code.
using CasADi
# Define your optimization problem using CasADi functions
x = MX.sym("x")
y = MX.sym("y")
z = x + y
# Create a CasADi function to evaluate the objective function
f = Function("f", [x, y], [z])
# Solve the optimization problem
result = f(2, 3)
println(result)
This approach is straightforward and allows you to leverage the full power of CasADi within your Julia code. However, it requires you to install the CasADi library separately and manage the dependencies between the Julia package and the CasADi library.
Option 2: Using CasADi as an External Library
If you prefer to use CasADi as an external library rather than a Julia package, you can do so by calling the CasADi functions through the Julia C API. This approach allows you to use CasADi without installing the CasADi Julia package, but it requires some additional setup.
# Load the CasADi library
const casadi = Libdl.dlopen("libcasadi.so")
# Define the CasADi functions using the Julia C API
casadi_function = Libdl.dlsym(casadi, :casadi_function)
# Define your optimization problem using CasADi functions
x = casadi_function("MX_sym", "x")
y = casadi_function("MX_sym", "y")
z = casadi_function("MX_add", x, y)
# Create a CasADi function to evaluate the objective function
f = casadi_function("Function", "f", [x, y], [z])
# Solve the optimization problem
result = casadi_function(f, 2, 3)
println(result)
This approach allows you to use CasADi without installing the CasADi Julia package, but it requires more manual setup and is less convenient compared to using the CasADi Julia package.
Option 3: Using CasADi through a Julia Wrapper
If you prefer a middle ground between the two previous options, you can use a Julia wrapper for CasADi. A Julia wrapper is a package that provides a higher-level interface to an external library, making it easier to use from Julia.
using CasADiWrapper
# Define your optimization problem using the CasADiWrapper functions
x = CasADiWrapper.MX.sym("x")
y = CasADiWrapper.MX.sym("y")
z = x + y
# Create a CasADiWrapper function to evaluate the objective function
f = CasADiWrapper.Function("f", [x, y], [z])
# Solve the optimization problem
result = f(2, 3)
println(result)
This approach provides a more convenient interface to CasADi compared to using the CasADi Julia package directly. However, it still requires you to install the CasADi library separately and manage the dependencies between the Julia wrapper package and the CasADi library.
After exploring these three options, it is clear that using the CasADi Julia package is the best choice. It provides the most straightforward and convenient way to use CasADi from Julia, without the need for additional setup or manual management of dependencies. Therefore, if you are working with Julia and need to use CasADi, installing the CasADi Julia package is the recommended approach.
