Arno Strouwen

Gradient by zygote of scalar expectation in batch mode

5

```jl using ForwardDiff using Zygote using OrdinaryDiffEq using DiffEqUncertainty using DiffEqSensitivity using Distributions using Cubature function f!(du,u,p,t) du[1] = p[1]*u[1] - p[2]*u[1]*u[2] #prey du[2] = -p[3]*u[2] + p[4]*u[1]*u[2] #predator end...

Feature request: Koopman expectation combined with collocation techniques

4

Is it possible to combine the Koopman expectation method with collocation techniques defined in DiffEqFlux for very efficient stochastic optimal control?

similar to LinearSolve

```jl using NonlinearSolve f(u, p) = u .* u .- 2.0 u0 = (1.0, 2.0) # brackets probB = NonlinearProblem(f, u0) solver = init(probB, Falsi()) # Can iterate the solver...

Optimize-then-Discretize for optimal control problems

1

For differential equations there already are both methods for OD and DO, that deal with the fact that time is continuous. But for infinite dimensional optimization problems the optimality criteria...

Rosenbrock interior point newton tutorial fails outdated MTK functions outdated DiffEqSensitivity

NonConvex package manager

2

``` pkg > add Optimization Optimization OptimizationFlux OptimizationNLopt OptimizationProblems OptimizationAlgorithms OptimizationGCMAES OptimizationNOMAD OptimizationSpeedMapping OptimizationBBO OptimizationMOI OptimizationOptimJL OptimizationCMAEvolutionStrategy OptimizationMetaheuristics OptimizationOptimisers OptimizationEvolutionary OptimizationMultistartOptimization OptimizationPolyalgorithms ``` Should NonConvex not be in here?

https://docs.sciml.ai/dev/modules/Integrals/tutorials/differentiating_integrals/ ``` julia using Integrals, ForwardDiff, FiniteDiff, Zygote, IntegralsCuba f(x,p) = sum(sin.(x .* p)) lb = ones(2) ub = 3ones(2) p = [1.5,2.0] function testf(p) prob = IntegralProblem(f,lb,ub,p) sin(solve(prob,CubaCuhre(),reltol=1e-6,abstol=1e-6)[1]) end...