This example uses the OptimizationOptimJL.jl package. See the Optim.jl page for details on the installation and usage.
ModelingToolkit.jl is a comprehensive system for symbolic modeling in Julia. Allows for doing many manipulations before the solver phase, such as detecting sparsity patterns, analytically solving parts of the model to reduce the solving complexity, and more. One of the types of system types that it supports is
OptimizationSystem, i.e. the symbolic counterpart to
OptimizationProblem. Let's demonstrate how to use the
OptimizationSystem to construct optimized
First we need to start by defining our symbolic variables, this is done as follows:
using ModelingToolkit, Optimization, OptimizationOptimJL @variables x y @parameters a b
We can now construct the
OptimizationSystem by building a symbolic expression for the loss function:
loss = (a - x)^2 + b * (y - x^2)^2 sys = OptimizationSystem(loss,[x,y],[a,b])
In order to turn it into a problem for numerical solutions, we need to specify what our parameter values are and the initial conditions. This looks like:
u0 = [ x=>1.0 y=>2.0 ] p = [ a => 6.0 b => 7.0 ]
And now we solve.
prob = OptimizationProblem(sys,u0,p,grad=true,hess=true) solve(prob,Newton())
It has a lot of other features like auto-parallelism and sparsification too. Plus you can hierarchically nest systems to have it generate huge optimization problems. Check out the ModelingToolkit.jl OptimizationSystem documentation for more information.