Differentiate through SecondOrderCone

Specific Domains Optimization (Mathematical)
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1

Hello :slight_smile:

I want to differentiate an Optimization Program. I use DiffOpt for this, which is built over JuMP. I have some data, that I want to fit by a function of a few parameters. This can be done with differentiating some SecondOrderCone(). It could be made with a fit program but I want to make my code work, as an exercise for later.

I build a JMP model with :

    model = Model(() -> DiffOpt.diff_optimizer(Hypatia.Optimizer))

The constraints are of the following form :

@constraint(model , [ T[i] , real(e) - real(val) ,  imag(e) - imag(val) ]   in SecondOrderCone() )

T[i] is a positive parameter, e is the function, and val is the data. if T[i] == 0, the fit is perfect.

We minimize sum( T )
I launch the optimisation optimize!(model)

For each constraint, I add the differentitate of the constraint. It is of the form :

MOI.set(model, DiffOpt.ForwardConstraintFunction(), cref, vec)

cref is the reference of the constraint, and vec is the derived vector.

And finally, I launch :

DiffOpt.forward_differentiate!(model)

Then I get the error :

ERROR: Trying to compute the forward differentiation on a model with termination status OPTIMIZE_NOT_CALLED

This error corresponds to the last line written here.
Where is my mistake ? What should I do to make it work ?
Thanks a lot !

2

Hi!
Could you provide a complete code with all the necessary imports, variable and function definitions, as well as the full error message?
Thanks :slight_smile:

3

Hello :slight_smile:

here is my code. Is it OK for you, do you need some explanations ? Thanks !

using DynmicPolynomials, JuMP,Hypatia,DiffOpt
import MathOptInterface; const MOI = MathOptInterface

function get_expect_with_deriv(Obs,
    _p0_r,_p0_i,
    _p1_r,_p1_i,
    _p2_r,_p2_i,
    _p3_r,_p3_i,
    _alpha_r,_alpha_i)

     # write here the computing of the value, and the grad_coeff which is a NamedTuple

    return e_val, grad_coeff
end

function diff_data_cat(dict_data, deg_max,
    p0_r, p0_i,
    p1_r, p1_i,
    p2_r, p2_i,
    p3_r, p3_i,
    alpha_r, alpha_i
)

    model = Model(() -> DiffOpt.diff_optimizer(Hypatia.Optimizer))

    # 
    Ncons = length(dict_data)
    @variable(model, T[1:Ncons] >= 0)

    soc_refs = NamedTuple[]  # vecteur générique de NamedTuple

    it = 1
    for (obs,val) in dict_data
        e, _ = get_expect_with_deriv(TermToExpr(obs), p0_r,p0_i,p1_r,p1_i,p2_r,p2_i,p3_r,p3_i,alpha_r , alpha_i)
                    # get (e) and it's derivative. Espectation with the parameters

        cref = @constraint(model,
            [ T[it],
                real(e) - real(val),
                imag(e) - imag(val)
            ] in SecondOrderCone()   # Check the distance between (e) and (val). Minimise it (with T[it] )
        )
        push!(soc_refs, (obs=obs, val=val, cref=cref, idx=it))
        it += 1
    end

    @objective(model, Min, sum(T))

    optimize!(model)

    for i_deriv in 1:8

        MOI.set(model, DiffOpt.ForwardObjectiveFunction(), 0.0) # The Objective is constant. T does not depend on the parameters p ... alpha

        for entry in soc_refs
            cref = entry.cref

            _, de = get_expect_with_deriv(TermToExpr(entry.obs),
                p0_r,p0_i, p1_r,p1_i, p2_r,p2_i, p3_r,p3_i, alpha_r,alpha_i
            )   # get the derivative of the expectation
            dval = ComplexF64(de[i_deriv])
            vec = Vector{Float64}([
                0.0,
                real(dval),   # ∂Re(e)/∂param
                imag(dval)    # ∂Im(e)/∂param
            ]) # get the derivative of T[it] ...
            
            MOI.set(model, DiffOpt.ForwardConstraintFunction(), cref, vec)
        end

        DiffOpt.forward_differentiate!(model) # compute the derivatives
        # here is the reference of the bug
        # Compute and store the grad
    end

    return objective_value(model), grad_T
end


ERROR: Trying to compute the forward differentiation on a model with termination status OPTIMIZE_NOT_CALLED
Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:35
 [2] forward_differentiate!(model::DiffOpt.Optimizer{MathOptInterface.Utilities.CachingOptimizer{…}})
   @ DiffOpt ~/.julia/packages/DiffOpt/FJjnq/src/moi_wrapper.jl:553
 [3] forward_differentiate!
   @ ~/.julia/packages/DiffOpt/FJjnq/src/jump_moi_overloads.jl:393 [inlined]
 [4] forward_differentiate!(model::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{…}, MathOptInterface.Utilities.UniversalFallback{…}})
   @ DiffOpt ~/.julia/packages/DiffOpt/FJjnq/src/jump_moi_overloads.jl:378
 [5] forward_differentiate!(model::Model)
   @ DiffOpt ~/.julia/packages/DiffOpt/FJjnq/src/jump_moi_overloads.jl:363
 [6] diff_data_cat(dict_data::Dict{…}, deg_max::Int64, p0_r::Float64, p0_i::Float64, p1_r::Float64, p1_i::Float64, p2_r::Float64, p2_i::Float64, p3_r::Float64, p3_i::Float64, alpha_r::Float64, alpha_i::Float64)
   @ Main ~/source/Cat_4legs/fit_cat.jl:236
 [7] test_diff_cat(alpha::Float64, Nmax::Int64, deg_max::Int64)
   @ Main ~/source/Cat_4legs/fit_cat.jl:289
 [8] top-level scope
   @ REPL[6]:1
4

@mbesancon this one is for you

1 Like
5

Or @joaquimg.

I’ve never really used DiffOpt, but I assume setting ForwardConstraintFunction modifies the model, which makes JuMP invalidate the solution. It’s possible this code path was never tested through JuMP, only MOI.

6

hello :slight_smile:
Maybe there is a problem between JuMP and DIffOpt. But both should work together, no ? Because DiffOpt is on the website of JuMP : jump.dev/DiffOpt.jl/stable/

I found a minimal code, that does not work, that you could try and test :

function test_SDP(T = Hypatia)

    model = Model(() -> DiffOpt.diff_optimizer(T.Optimizer))

    @variable(model , t >= 0)
    cons = @constraint(model , [t 1.0 ; 1.0 t] in PSDCone())
    @objective(model, Min , t)
    optimize!(model)

    MOI.set(model, DiffOpt.ForwardConstraintFunction(), cons, [0.0 1.0 ; 1.0 0.0 ])

    DiffOpt.forward_differentiate!(model) # compute the derivatives

end

I don’t know how to make it work …
Thanks for the help !