Optimization.jl
Optimization.jl copied to clipboard
SciML
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable inte...
It would be useful to be able to use `OptimizationBBO` without having all the output printed to stdout. In order to do this, we can use the `TraceMode` keyword argument,...
Hello, i have been lately running the docu for parameter optimization of ODEs https://sensitivity.sciml.ai/dev/ode_fitting/optimization_ode/ However, i tried to decrease the maxiters to 5 for multiple algorithms, but none of them...
The way an optimization problem with inequality constraints (written symbolically using the `OptimizationSystem` functionality of `ModelingToolkit`) is translated into an `OptimizationProblem` and conveyed to the solver wrapper (for instance `OptimizationMOI`)...
I have been trying to use Optimization.jl with Optim.jl to perform global optimization with `ParticleSwarm()`, with MethodOfLines.jl generating the data for the loss, and running the forward problem. I have...
The `NOMADOpt()` example in http://optimization.sciml.ai/dev/optimization_packages/nomad/ doesn't seem to work: ``` using Optimization using OptimizationNOMAD rosenbrock(x, p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2 x0 = zeros(2) p...
There is currently no (little? Haven't tried all the solvers) support for scalar inputs, e.g. ``` julia> f = (u, p) -> u^2; u0 = 0.0; prob = OptimizationProblem(f, u0);...
Though it only focuses on hessians, combining with a gradient call from another package and hessian from https://github.com/KristofferC/HyperHessians.jl would be interesting for second order methods.
The instantiate function for most AD backends only populates a field if it is `nothing`. On the other hand, the instantiate function for NoAD only processes those fields that are...
Apart from `ForwardDiff.jl` no other AD backend supports constraints. Is this just a lack of implementation or is there a deeper issue?
The docs mention an out-of-place option, but it seems like user-provided gradients, etc. are all assumed in-place at the moment. For example, ``` using Optimization using NLopt using OptimizationNLopt obj(x,...