OptimizationODE package for ODE solvers #916
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OptimizationODE.jl added and package created for the same.
Added basic tests for the gradient descent code in OptimizationODE.jl
Project.toml for the initial OptimizationODE.jl code.
Added algorithm functionality along with helper functions for: ODEGradientDescent, RKChebyshevDescent, RKAccelerated, PRKChebyshevDescent.
Updated tests for the new functions and AD test for ODEGradientDescent.
Updated code for the 4 algs, removing redundant code.
Updated tests using the ODEOptimizer wrapper.
added OrdinaryDiffEq for reusing ODE solvers.
Updated struct and constructors, the ODEOptimizer now takes type instead of instance.
updated tests.
ChrisRackauckas
requested changes
May 28, 2025
Add callback, progress, struct accepts type instead of insatance, maxiters passed.
Tests match the latest ODE code with callbacks and maxiters.
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| function SciMLBase.__init(prob::OptimizationProblem, opt::ODEOptimizer, data=Optimization.DEFAULT_DATA; | ||
| η=0.1, dt=nothing, tmax=100.0, callback=Optimization.DEFAULT_CALLBACK, progress=false, |
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| η=0.1, dt=nothing, tmax=100.0, callback=Optimization.DEFAULT_CALLBACK, progress=false, | |
| dt=nothing, tmax=100.0, callback=Optimization.DEFAULT_CALLBACK, progress=false, |
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| η = get(cache.solver_args, :η, 0.1) | ||
| dt = get(cache.solver_args, :dt, nothing) | ||
| tmax = get(cache.solver_args, :tmax, 100.0) |
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why is this set? Is that standard? That seems really odd.
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Docs next |
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Checklist
contributor guidelines, in particular the SciML Style Guide and
COLPRAC.
Additional context
This is a package for developing ODE solvers for Optimization with methods like Gradient Descent, Chebyshev stabilization and the Runge-kutta acceleration.