How to set lower and upper bound for SimulatedAnnealing OptimizationProblem's? Tired "lu=" and "ub=" but doesn't seem to work

Specific Domains Optimization (Mathematical)
1

This is the sampel code:

using GalacticOptim, Optim
function tmp(x,p)
   return (x[1]-2)^2 
end
prob = OptimizationProblem(tmp,[4.],lb=[3],ub=[6])
res = solve(prob,SimulatedAnnealing())

the result is correct 1.9982228757130818. However, I set a lower bound =3, so would expect it to step here. How do I get the lower bound to work?

Similarily, for:

using GalacticOptim, Optim
function tmp(x,p)
   return -(x[1]-2)^2 
end
prob = OptimizationProblem(tmp,[4.],lb=[3],ub=[6])
res = solve(prob,SimulatedAnnealing())

I would expect to just hit the upper bound, but I get the answer 400.7172021819915.

Am I missing something obvious here?
(also at https://github.com/SciML/GalacticOptim.jl/issues/171)

2

The modified program

using GalacticOptim, Optim
function tmp(x,p)
   return (x[1]-2)^2
end
fun = OptimizationFunction(tmp, GalacticOptim.AutoForwardDiff())
prob = OptimizationProblem(fun,[4.],lb=[3],ub=[6])
res = solve(prob,Fminbox())
println(res)

using GalacticOptim, Optim
function tmp(x,p)
   return -(x[1]-2)^2
end
fun = OptimizationFunction(tmp, GalacticOptim.AutoForwardDiff())
prob = OptimizationProblem(fun,[4.],lb=[3],ub=[6])
res = solve(prob,Fminbox())
println(res)

returns

retcode: Default
u: [3.0000000013333334]
Final objective value:     1.0000000026666669

retcode: Default
u: [5.999999999666667]
Final objective value:     -15.999999997333333

Furthermore Optim.jl’s documentation https://github.com/JuliaNLSolvers/Optim.jl/blob/adc5b277b3f915c25233b45f8f2dd61006815e63/docs/src/algo/simulated_annealing.md
does not mention support for bounds. This looks like a documentation problem to me.

1 Like
3

I found out about the bounds at OptimizationProblem · GalacticOptim.jl, but you might be right and those not applying to the SimulatedAnnealing algorithm.

I have a problem where derivatives don’t make sense, so I figured I was limited to that one and NelderMead (https://galacticoptim.sciml.ai/stable/local_optimizers/local_derivative_free/), which I ran into other problems with (it errors at the HPC I have access to, I’ve asked the HPC people for help with interpreting the error message the HPC yielded).

4

Open an issue. GalacticOptim should just wrap the algorithm in an FMinBox automatically if you add bounds.

3 Likes
5

Got it :+1:

6

There is a bounded simulated annealing algorithm in Optim.jl called SAMIN. An example of how to call it would be
opt = Optim.optimize(obj, lb, ub, θ, SAMIN())
where ib, ub and θ are all vectors.

3 Likes
7

Thanks for pointing that out, I will try to get it to work.