# JuMP - Nested Optimization with user defined functions

Hi!

I have a Nested Optimization problem that I am trying to solve using JuMP. Inside, I have a linear problem with power constraints where I use JuMP + Mosek to solve. Outside, I have a concave problem defined on a set of linear constraints. The inner problem gives me both the value of the function conditional on the variable I am optimizing in the outer problem, and its gradients.

Ipopt seems like the natural optimizer for this problem. However, I have been using NLOpt because I cannot get the user-defined function to work.

This is not a MWE, but I am trying to do something like this:

using Ipopt, JuMP

@everywhere function f1_val(data::Data, x::Vector{Float64}, n::Int)
Eπ, Eν = 0.0, zeros(data.I)
for s in 1:data.Sexp
temp = f2(data, x, s, n)
Eπ += temp[1]
Eν .+= temp[2]
end
return Eπ / data.Sexp - sum(data.α[:,n]  .*x)
end

@everywhere function f1_grad(data::Data, x::Vector{Float64}, n::Int)
Eπ, Eν = 0.0, zeros(data.I)
for s in 1:data.Sexp
temp = f2(data, x, s, n)
Eπ += temp[1]
Eν .+= temp[2]
end
return  Eν ./ data.Sexp .- data.α[:,n]
end


I woiuld like to find the vector x that maximizes f1_val.

I don’t fully understand your example because I don’t know what f2 ore the data are, but here’s an example of nested optimization problems via user-defined functions:

using JuMP
import Ipopt

function bilevel_problem()
last_x, last_f, last_y = nothing, 0.0, [NaN, NaN]
function _update(x...)
if last_x == x
return last_y
end
model = Model(Ipopt.Optimizer)
set_silent(model)
@variable(model, y[1:2])
@NLobjective(
model,
Max,
y[1] * x[1]^2 + y[2] * x[2]^2 - x[1] * y[1]^4 - 2 * x[2] * y[2]^4,
)
@constraint(model, (y[1] - 10)^2 + (y[2] - 10)^2 <= 25)
optimize!(model)
@assert termination_status(model) == LOCALLY_SOLVED
last_x = x
last_f = objective_value(model)
last_y[1] = value(y[1])
last_y[2] = value(y[2])
return last_y
end
function f(x...)
_update(x...)
return last_f
end
function ∇f(g, x...)
_update(x...)
g[1] = 2 * x[1] * last_y[1] - last_y[1]^4
g[2] = 2 * x[2] * last_y[2] - 2 * last_y[2]^4
return
end
return _update, f, ∇f
end

function bench_bilevel_nlp()
model = Model(Ipopt.Optimizer)
set_optimizer_attribute(model, "tol", 1e-5)
@variable(model, x[1:2] >= 0)
subproblem, f, ∇f = bilevel_problem()
register(model, :bilevel_f, 2, f, ∇f)
@NLobjective(model, Min, bilevel_f(x[1], x[2]) + x[1]^2 + x[2]^2)
optimize!(model)
@assert termination_status(model) == LOCALLY_SOLVED
println(objective_value(model))
println("x = ", value.(x))
println("y = ", subproblem(value.(x)...))
return
end


Hi Odow,

I wonder how to register an inner JuMP optimization as the nested function and use the user-defined function in constraint or objective of the outer JuMP optimization. The motivation for doing this lies in the feature of general economic models: A hacky guide to using automatic differentiation in nested optimization problems - #6 by jeffreyesun.

I am working with a maximum likelihood case where the likelihood sums across M markets

\max_{\theta, \delta}\sum_{m=1}^M l(\theta,\delta_m)

\theta is a low dimensional vector with 5 to 10 parameters. \delta are market-m specific; each market \delta_m contains about 15 parameters that correspond to available products in the market m.

Writing the whole optimization in one JuMP instance with sparse index

function mle(data)
model = Model(Ipopt.Optimizer)
@variable(model, θ[1:5])
@variable(model, δ[m in market_idset, j in product_market[m]])
@variable(model, logli[m in market_idset])
...
@NLobjective(model, Min, -sum(logli[m] for m in market_idset))
end


It works well when M is small. (less than 4 minutes with M=5 markets, 300,000 total variables, 16G MacBook Pro). However, my full sample has 300~400 markets and JuMP would never start the iteration. The total number of variables reaches at a scale of ten million in the full sample case. I am considering whether a nested optimization could improve the performance.

For each market, the log likelihood l(\delta_m,\theta) is convex in \delta_m. Fixing \theta, \max_{\delta_m}l(\delta_m,\theta) takes about 10 seconds to find the local optimal solution for \delta_m. I would like to rephrase the JuMP optimization in the light of the nested structure. An incomplete/incorrect JuMP I am trying to build from your example follows:

function outer_f(data)
outer_model = Model(Ipopt.Optimizer)
@variable(outer_model, θ[1:5])
@variable(outer_model, logli[m in market_idset])
@register(outer_model,:inner_f,5,inner_JuMP_delta)
for m in mkt_idset
@constraint(outer_model,logli[m] == inner_f(θ)[m][1])
end
@NLobjective(outer_model,Min,-sum(logli[m] for m in mkt_idset))
optimize(outer_model)
end

function inner_JuMP_delta(para...,)
result_obj = zeros(num_markets)
result_delta = zeros(num_markets, num_products)
for m in market_idset
inner_model = Model(Ipopt.Optimizer)
@variable(inner_model, δ in product_market[m])
...
@NLobjective(log(...))
obj_result[m] = objective_value(inner_model)
result_prod[m,:] = value.(δ)
end
result, result_delta
end



The main issue is that how to register a rather complicated user-definition function involving nonlinear expression, data structure (like Dictionary I am heavily using) and also returning a large array. If JuMP couldn’t work, I am considering using Optim,jl in the outer optimization. I tried it with M=5 and the derivative-free approach, but it was significantly slower than the one JuMP optimization mle().

Thanks for any thought!

A couple of points:

• User-defined functions must return scalar outputs.
• You probably need to define a separate function for each market
• You can’t automatically differentiate a JuMP model, so you’ll need to provide the explicit gradient of the loss function

I don’t have any data so I haven’t tested this, but hopefully it points you in the right direction. If you still have questions, make a new post with reproducible data.

There are various pointers in the documentation about this, take a read of:

function loss(m, θ...)
model = Model(Ipopt.Optimizer)
@variable(model, δ[product_market[m]])
@NLobjective(model, Max, log(δ))
optimize!(model)
return objective_value(model)
end

function ∇loss(m, g, θ...)
model = Model(Ipopt.Optimizer)
@variable(model, δ[product_market[m]])
@NLobjective(model, Max, log(δ))
optimize!(model)
g .= 0.0 # TODO
return
end

model = Model(Ipopt.Optimizer)
@variable(model, θ[1:5])
markets = [(θ...) -> loss(m, θ...) for m in markets_idset]
∇markets = [(g, θ...) -> ∇loss(m, g, θ...) for m in markets_idset]
expr = Expr(:call, :+)
for (i, (f, ∇f)) in enumerate(zip(markets, ∇markets))
f_sym = Symbol("f_$(i)") register(model, f_sym, 1, f, ∇f) push!(expr.args, :($(f_sym)($(δ)...))) end set_nonlinear_objective(model, MOI.MIN_SENSE, Expr(:call, :-, expr))  Hi @odow . I finally implemented your suggestion for the nested optimization, setting up as a bilevel problem. The innermost nest is solving a linear function defined on a set of linear inequalities and power cones. The outer function is minimizing a continuous convex function with non-negativity constraints, and gradients are available. So, in principle, this problem is suited to be solved using gradient-based nonlinear convex solvers. For solving this problem, I am currently using NLopt with :LD_MMA. But this cannot be the most efficient solution for this problem. My interest in being able to successfully write it as a nested optimization problem is that it allows me to use JuMP and leverage the different optimizers for convex functions that are available. However, using Ipopt the performance is dismal. Do you see any obvious performance flaws in the code below? Can you suggest any other more efficient way to solve the outer function? See the MWE below using JuMP, Ipopt, LinearAlgebra, Random, Distributions, BenchmarkTools, MosekTools, NLopt, Plots, FLoops Threads.nthreads() Random.seed!(12345) const K = 100 const J = 50 const S = 25 const z=rand(K,S) .+ 1.0 const α₁=rand(K) .+ 1.0 const α₂=rand(K).*.1 const σ = 3.0 function distmat(J,K) Distances = zeros(J,K) Random.seed!(7777) coordinates_x = hcat([x for x = 1:J],fill(0.0,J)) coordinates_y = hcat([x for x = 1:K].*J/K,fill(0.0,K)) for j = 1:J, l=1:K Distances[j,l] = sqrt((coordinates_x[j,1]-coordinates_y[l,1])^2+(coordinates_x[j,2]-coordinates_y[l,2])^2) end return 1 .+ .1*Distances end const τ = distmat(J,K) function innermost(x,σ,τ,z,s) (J,K) = size(τ) opt = Model(Mosek.Optimizer) set_optimizer_attribute(opt, "QUIET", false) set_optimizer_attribute(opt, "INTPNT_CO_TOL_DFEAS", 1e-7) set_optimizer_attribute(opt, "MSK_IPAR_INTPNT_MULTI_THREAD", false) set_silent(opt) c = ones(J) @variable(opt, ν[1:K] >= 0) @variable(opt, μ[1:J]>= 0) @variable(opt, t[1:J]>= 0) @objective(opt, Min, ν'*x .+ c'*t) @constraint( opt, c[i = i=1:K, j=1:J], τ[j,i]/z[i,s] + τ[j,i]*ν[i] - μ[j] >= 0, ) @inbounds for j = 1 : J @constraint(opt, [t[j],μ[j],1] in MOI.PowerCone(1/σ)) end JuMP.optimize!(opt) return objective_value(opt), value.(ν)[1:K], value.(μ)[1:J], dual.(c) end function inner(x,σ,τ,z,α₁,α₂) Eπ, Eν = 0.0, zeros(K) for s in 1:S temp = innermost(x,σ,τ,z,s) Eπ += temp[1] Eν .+= temp[2] end return Eπ / S .- sum(α₁.*x) .- sum(α₂.*x.^2) , Eν ./ S .- α₁ .- 2 .*α₂.*x end function outer(guess,σ,τ,z,α₁,α₂) function f1(x::Vector, grad::Vector) temp = inner(x,σ,τ,z,α₁,α₂) if length(grad) > 0 grad[:]= -temp[2] end return -temp[1] end function optimization_step(guess) opt = Opt(:LD_MMA, K) opt.lower_bounds = zeros(K) opt.xtol_rel = sqrt(eps()) opt.ftol_rel = eps() opt.min_objective = f1 (minf,minx,ret) = NLopt.optimize(opt, guess) numevals = opt.numevals # the number of function evaluations println("got$minf at $minx after$numevals iterations in (returned $ret)") return minx, minf end minx, minf = optimization_step(guess) return minx, -minf end @btime innermost(ones(K),σ,τ,z,1) ; @btime inner(ones(K),σ,τ,z,α₁,α₂) ; (xstar,fstar) = @btime outer(ones(K),σ,τ,z,α₁,α₂) ; # Using Ipopt and Bilevel Problem in JUMP function bilevel_problem(σ,τ,z,α₁,α₂) last_x, last_f, last_y = nothing, 0.0, [NaN, NaN] function _update(x...) if last_x == x return last_y end (last_f,last_y) = inner(collect(x),σ,τ,z,α₁,α₂) last_x = x return last_y end function f(x...) _update(x...) return last_f end function ∇f(g, x...) _update(x...) g .= -last_y return end return _update, f, ∇f end function bench_bilevel_nlp(σ,τ,z,α₁,α₂) model = Model(Ipopt.Optimizer) set_optimizer_attribute(model, "tol", 1e-5) @variable(model, X[1:K] >= 0) subproblem, f, ∇f = bilevel_problem(σ,τ,z,α₁,α₂) register(model, :bilevel_f, K, f, ∇f) @NLobjective(model, Min, bilevel_f(X...)) JuMP.optimize!(model) @assert termination_status(model) == LOCALLY_SOLVED println(objective_value(model)) println("x = ", value.(x)) println("y = ", subproblem(value.(x)...)) return end bench_bilevel_nlp(σ,τ,z,α₁,α₂)  The performance problem is likely two-fold. First, every evaluation of the inner problem requires building and solving 25 models with Mosek. Second, you have 100 X parameters, so computing the gradient requires on the order of 100 * 25 Mosek solves. And then Ipopt can be quite expensive with the number of function evaluations, trying to do line stepping, etc. I haven’t tested the code because I don’t have Mosek, but I would try solving the single, larger problem: function innermost(x,σ,τ,z,S) (J,K) = size(τ) opt = Model(Mosek.Optimizer) set_optimizer_attribute(opt, "QUIET", false) set_optimizer_attribute(opt, "INTPNT_CO_TOL_DFEAS", 1e-7) set_optimizer_attribute(opt, "MSK_IPAR_INTPNT_MULTI_THREAD", false) set_silent(opt) c = ones(J) @variable(opt, ν[1:K, 1:S] >= 0) @variable(opt, μ[1:J, 1:S] >= 0) @variable(opt, t[1:J, 1:S] >= 0) @expression(opt, obj[s=1:S], ν[:,s]' * x + sum(t[:,s])) @objective(opt, Min, sum(obj)) @constraint( opt, c[i=1:K, j=1:J, s=1:S], τ[j,i] / z[i,s] + τ[j,i] * ν[i] - μ[j] >= 0, ) @constraint(opt, [j=1:J, s=1:S], [t[j,s], μ[j,s], 1] in MOI.PowerCone(1 / σ)) JuMP.optimize!(opt) return objective_value(opt), sum(value.(ν[:,s]) for s in 1:S) end function inner(x,σ,τ,z,α₁,α₂) Eπ, Eν = innermost(x,σ,τ,z,S) return Eπ / S .- sum(α₁.*x) .- sum(α₂.*x.^2) , Eν ./ S .- α₁ .- 2 .*α₂.*x end  You could improve things even further by building the opt problem outside the loop and updating only the objective between solves, but see if the code above helps first. Hi @odow , thanks for your answer. I’ve implemented your clever suggestion in the MWE below. Now, the code runs successfully. However, in implementing the nested optimization I still have some sort of issue. I do not know whether the problem is a bug in bilevel_problem or bench_bilevel_nlp, but it takes almost as twice as long. For the second issue that you are pointing out, do you have any particular suggestion? The function that I am minimizing is continuous and convex on the vector X, and I have the gradients which are the vector of \nu, so the minimization should be very conventional, aside from the fact that it is nested. I am currently using Ipopt, because it is standard, however, if I end up solving this issue with the nested optimization, my plan is to use knitro. But if any other optimizer comes to mind, I would appreciate your suggestions. The MWE is here: using JuMP, Ipopt, LinearAlgebra, Random, Distributions, BenchmarkTools, MosekTools, NLopt, Plots, FLoops Threads.nthreads() Random.seed!(12345) const K = 100 const J = 50 const S = 25 const z=rand(K,S) .+ 1.0 const α₁=rand(K) .+ 1.0 const α₂=rand(K).*.1 const σ = 3.0 function distmat(J,K) Distances = zeros(J,K) Random.seed!(7777) coordinates_x = hcat([x for x = 1:J],fill(0.0,J)) coordinates_y = hcat([x for x = 1:K].*J/K,fill(0.0,K)) for j = 1:J, l=1:K Distances[j,l] = sqrt((coordinates_x[j,1]-coordinates_y[l,1])^2+(coordinates_x[j,2]-coordinates_y[l,2])^2) end return 1 .+ .1*Distances end const τ = distmat(J,K) function innermost(x,σ,τ,z,s) (J,K) = size(τ) opt = Model(Mosek.Optimizer) set_optimizer_attribute(opt, "QUIET", false) set_optimizer_attribute(opt, "INTPNT_CO_TOL_DFEAS", 1e-7) set_optimizer_attribute(opt, "MSK_IPAR_INTPNT_MULTI_THREAD", false) set_silent(opt) c = ones(J) @variable(opt, ν[1:K] >= 0) @variable(opt, μ[1:J]>= 0) @variable(opt, t[1:J]>= 0) @objective(opt, Min, ν'*x .+ sum(t)) @constraint( opt, c[i = i=1:K, j=1:J], τ[j,i]/z[i,s] + τ[j,i]*ν[i] - μ[j] >= 0, ) @inbounds for j = 1 : J @constraint(opt, [t[j],μ[j],1] in MOI.PowerCone(1/σ)) end JuMP.optimize!(opt) return objective_value(opt), value.(ν)[1:K], value.(μ)[1:J], dual.(c) end function inner(x,σ,τ,z,α₁,α₂) Eπ, Eν = 0.0, zeros(K) for s in 1:S temp = innermost(x,σ,τ,z,s) Eπ += temp[1] Eν .+= temp[2] end return Eπ / S .- sum(α₁.*x) .- sum(α₂.*x.^2) , Eν ./ S .- α₁ .- 2 .*α₂.*x end function innermost1(x,σ,τ,z,S) (J,K) = size(τ) opt = Model(Mosek.Optimizer) set_optimizer_attribute(opt, "QUIET", false) set_optimizer_attribute(opt, "INTPNT_CO_TOL_DFEAS", 1e-7) set_optimizer_attribute(opt, "MSK_IPAR_INTPNT_MULTI_THREAD", false) set_silent(opt) @variable(opt, ν[1:K, 1:S] >= 0) @variable(opt, μ[1:J, 1:S] >= 0) @variable(opt, t[1:J, 1:S] >= 0) @expression(opt, obj[s=1:S], ν[:,s]' * x + sum(t[:,s])) @objective(opt, Min, sum(obj)) @constraint( opt, c[i=1:K, j=1:J, s=1:S], τ[j,i] / z[i,s] + τ[j,i] * ν[i,s] - μ[j,s] >= 0, ) @constraint(opt, [j=1:J, s=1:S], [t[j,s], μ[j,s], 1] in MOI.PowerCone(1 / σ)) JuMP.optimize!(opt) return objective_value(opt), sum(value.(ν[:,s]) for s in 1:S) end function inner1(x,σ,τ,z,α₁,α₂) Eπ, Eν = innermost1(x,σ,τ,z,S) return Eπ / S .- sum(α₁.*x) .- sum(α₂.*x.^2) , Eν ./ S .- α₁ .- 2 .*α₂.*x end function outer(guess,σ,τ,z,α₁,α₂) function f1(x::Vector, grad::Vector) temp = inner(x,σ,τ,z,α₁,α₂) if length(grad) > 0 grad[:]= -temp[2] end return -temp[1] end function optimization_step(guess) opt = Opt(:LD_SLSQP, K) opt.lower_bounds = zeros(K) opt.xtol_rel = sqrt(eps()) opt.ftol_rel = eps() opt.min_objective = f1 (minf,minx,ret) = NLopt.optimize(opt, guess) numevals = opt.numevals # the number of function evaluations #println("got$minf at $minx after$numevals iterations in (returned $ret)") return minx, minf end minx, minf = optimization_step(guess) return minx, -minf end function outer1(guess,σ,τ,z,α₁,α₂) function f1(x::Vector, grad::Vector) temp = inner1(x,σ,τ,z,α₁,α₂) if length(grad) > 0 grad[:]= -temp[2] end return -temp[1] end function optimization_step(guess) opt = Opt(:LD_SLSQP, K) opt.lower_bounds = zeros(K) opt.xtol_rel = sqrt(eps()) opt.ftol_rel = eps() opt.min_objective = f1 (minf,minx,ret) = NLopt.optimize(opt, guess) numevals = opt.numevals # the number of function evaluations #println("got$minf at $minx after$numevals iterations in (returned \$ret)")

return minx, minf

end

minx, minf = optimization_step(guess)

return minx, -minf

end

println("Looping over S in Inner")

@btime innermost(ones(K),σ,τ,z,1) ;
@btime inner(ones(K),σ,τ,z,α₁,α₂) ;
(xstar,fstar) = @btime outer(ones(K),σ,τ,z,α₁,α₂) ;

println("fstar:", fstar)

println("Looping over S in Innermost")

@btime innermost1(ones(K),σ,τ,z,S) ;
@btime inner1(ones(K),σ,τ,z,α₁,α₂) ;
(xstar1,fstar1) = @btime outer1(ones(K),σ,τ,z,α₁,α₂) ;

println("fstar1:", fstar1)

println("Using Ipopt and Bilevel Problem in JUMP")

function bilevel_problem(σ,τ,z,α₁,α₂)
last_x, last_f, last_y = nothing, 0.0, [NaN, NaN]
function _update(x...)
if last_x == x
return last_y
end
(last_f,last_y) = inner1(collect(x),σ,τ,z,α₁,α₂)
last_x = x
return last_y
end
function f(x...)
_update(x...)
return -last_f
end
function ∇f(g, x...)
_update(x...)
g .= -last_y
return
end
return _update, f, ∇f
end

function bench_bilevel_nlp(σ,τ,z,α₁,α₂)
model = Model(Ipopt.Optimizer)
set_optimizer_attribute(model, "tol", 1e-3)
@variable(model, X[1:K] >= 0)
subproblem, f, ∇f = bilevel_problem(σ,τ,z,α₁,α₂)
register(model, :bilevel_f, K, f, ∇f)
@NLobjective(model, Min, bilevel_f(X...))
JuMP.optimize!(model)
@assert termination_status(model) == LOCALLY_SOLVED
println(objective_value(model))
#println("x = ", value.(X))
#println("y = ", subproblem(value.(X)...))
return value.(X), -subproblem(value.(X)...)
end

(xstar2,fstar2) = @btime bench_bilevel_nlp(σ,τ,z,α₁,α₂)

println("fstar2:", fstar2)



and the output is here,

Looping over S in Inner

81.041 ms (332353 allocations: 20.98 MiB)
1.655 s (8308851 allocations: 524.37 MiB)
43.999 s (377874883 allocations: 23.28 GiB)

fstar
13.65302

Looping over S in Innermost
2.426 s (7380607 allocations: 491.34 MiB)
2.408 s (7380610 allocations: 491.35 MiB)
39.876 s (250960869 allocations: 16.31 GiB)

fstar1
13.65304

This is Ipopt version 3.14.4, running with linear solver MUMPS 5.4.1.

Number of nonzeros in equality constraint Jacobian...:        0
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:        0

Total number of variables............................:      100
variables with only lower bounds:      100
variables with lower and upper bounds:        0
variables with only upper bounds:        0
Total number of equality constraints.................:        0
Total number of inequality constraints...............:        0
inequality constraints with only lower bounds:        0
inequality constraints with lower and upper bounds:        0
inequality constraints with only upper bounds:        0

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
0 -4.6564070e+00 0.00e+00 1.00e+00   0.0 0.00e+00    -  0.00e+00 0.00e+00   0
1 -7.4529206e+00 0.00e+00 1.59e-02  -1.7 1.79e-02    -  1.00e+00 1.00e+00f  1
2 -8.9898544e+00 0.00e+00 1.80e-03  -3.5 2.25e-02    -  1.00e+00 1.00e+00f  1
3 -1.0099893e+01 0.00e+00 1.30e-03  -4.8 4.62e-02    -  1.00e+00 1.00e+00f  1
4 -1.1409563e+01 0.00e+00 6.85e-04  -4.8 1.07e-01    -  9.80e-01 1.00e+00f  1
5 -1.2170621e+01 0.00e+00 4.57e-04  -4.8 1.67e-01    -  9.97e-01 7.67e-01f  1
6 -1.2623251e+01 0.00e+00 3.87e-04  -4.6 3.02e-01    -  1.00e+00 8.88e-01f  1
7 -1.3059052e+01 0.00e+00 2.60e-04  -4.9 2.20e-01    -  9.91e-01 1.00e+00f  1
8 -1.3405223e+01 0.00e+00 1.94e-04  -5.4 1.48e-01    -  9.99e-01 9.00e-01f  1
9 -1.3517947e+01 0.00e+00 2.28e-04  -5.8 1.80e-01    -  1.00e+00 6.30e-01f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
10 -1.3593800e+01 0.00e+00 1.82e-04  -6.0 7.27e-02    -  9.99e-01 9.79e-01f  1
11 -1.3622958e+01 0.00e+00 3.63e-04  -6.7 5.59e-02    -  1.00e+00 6.07e-01f  1
12 -1.3269251e+01 0.00e+00 2.93e-04  -5.1 1.03e-01    -  9.14e-01 1.00e+00f  1
13 -1.3355551e+01 0.00e+00 1.48e-04  -5.2 5.45e-02    -  1.00e+00 1.00e+00f  1
14 -1.3369108e+01 0.00e+00 1.21e-04  -5.2 4.40e-02    -  1.00e+00 1.00e+00f  1
15 -1.3372775e+01 0.00e+00 6.94e-05  -5.2 1.87e-02    -  1.00e+00 1.00e+00f  1
16 -1.3372001e+01 0.00e+00 2.99e-05  -5.2 1.25e-02    -  1.00e+00 1.00e+00f  1
17 -1.3624447e+01 0.00e+00 1.98e-04  -7.8 7.08e-02    -  8.95e-01 9.91e-01f  1
18 -1.3643050e+01 0.00e+00 4.01e-04  -6.8 4.97e-02    -  1.00e+00 8.90e-01f  1
19 -1.3360714e+01 0.00e+00 3.84e-04  -5.2 9.15e-02    -  9.45e-01 1.00e+00f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
20 -1.3428833e+01 0.00e+00 1.19e-04  -5.3 3.37e-02    -  1.00e+00 1.00e+00f  1
21 -1.3436063e+01 0.00e+00 7.55e-05  -5.3 1.66e-02    -  1.00e+00 1.00e+00f  1
22 -1.3435569e+01 0.00e+00 4.03e-05  -5.3 6.90e-03    -  1.00e+00 1.00e+00f  1
23 -1.3635171e+01 0.00e+00 2.78e-04  -7.8 7.46e-02    -  8.74e-01 9.89e-01f  1
24 -1.3644960e+01 0.00e+00 5.63e-04  -7.8 4.55e-02    -  9.43e-01 5.52e-01f  1
25 -1.3575224e+01 0.00e+00 2.74e-04  -5.8 3.06e-02    -  1.00e+00 1.00e+00f  1
26 -1.3589880e+01 0.00e+00 7.45e-05  -5.8 1.97e-02    -  1.00e+00 1.00e+00f  1
27 -1.3591803e+01 0.00e+00 3.37e-05  -5.8 1.72e-02    -  1.00e+00 1.00e+00f  1
28 -1.3592033e+01 0.00e+00 4.62e-05  -5.8 7.57e-03    -  1.00e+00 1.00e+00f  1
29 -1.3592075e+01 0.00e+00 4.83e-06  -5.8 3.73e-03    -  1.00e+00 1.00e+00f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
30 -1.3649657e+01 0.00e+00 7.04e-05  -7.8 1.85e-02    -  9.79e-01 1.00e+00f  1
31 -1.3652124e+01 0.00e+00 5.60e-04  -8.9 1.36e-02    -  1.00e+00 7.51e-01f  1

Number of Iterations....: 31

(scaled)                 (unscaled)
Objective...............:  -2.1371882502778616e-02   -1.3652123983924689e+01
Dual infeasibility......:   5.5983821986408506e-04    3.5761851056080318e-01
Constraint violation....:   0.0000000000000000e+00    0.0000000000000000e+00
Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00
Complementarity.........:   7.2637672397801354e-08    4.6400147920968588e-05
Overall NLP error.......:   5.5983821986408506e-04    3.5761851056080318e-01

Number of objective function evaluations             = 32
Number of objective gradient evaluations             = 32
Number of equality constraint evaluations            = 0
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 0
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 0
Total seconds in IPOPT                               = 75.508

75.528 s

fstar2
13.6521



Finally, do you have any intuition for why the function inner is faster that inner1?

Again, I haven’t tried because I don’t have Mosek, but how about something like this:

function bilevel_problem(σ,τ,z,α₁,α₂)
last_x, last_f, last_y = nothing, 0.0, [NaN, NaN]

(J,K) = size(τ)
opt = Model(Mosek.Optimizer)
set_optimizer_attribute(opt, "QUIET", false)
set_optimizer_attribute(opt, "INTPNT_CO_TOL_DFEAS", 1e-7)
set_optimizer_attribute(opt, "MSK_IPAR_INTPNT_MULTI_THREAD", false)
set_silent(opt)
@variable(opt, ν[1:K, 1:S] >= 0)
@variable(opt, μ[1:J, 1:S] >= 0)
@variable(opt, t[1:J, 1:S] >= 0)
@constraint(
opt,
c[i=1:K, j=1:J, s=1:S],
τ[j,i] / z[i,s] + τ[j,i] * ν[i,s] - μ[j,s] >= 0,
)
@constraint(opt, [j=1:J, s=1:S], [t[j,s], μ[j,s], 1] in MOI.PowerCone(1 / σ))

function _update(x...)
if last_x == x
return last_y
end
@objective(
opt,
Min,
sum(sum(ν[k,s] * x[k] for k in 1:K) + sum(t[:,s]) for s in 1:S),
)
JuMP.optimize!(opt)
Eπ = objective_value(opt)
Eν = sum(value.(ν[:,s]) for s in 1:S)
last_f = Eπ / S .- sum(α₁.*x) .- sum(α₂.*x.^2)
last_y = Eν ./ S .- α₁ .- 2 .*α₂.*x
last_x = x
return last_y
end
function f(x...)
_update(x...)
return -last_f
end
function ∇f(g, x...)
_update(x...)
g .= -last_y
return
end
return _update, f, ∇f
end


Finally, do you have any intuition for why the function inner is faster that inner1 ?

This is a tricky question. Is it faster to solve S small problems, or 1 problem S times larger?

It seems like your outer1 was faster (admittedly, only just), so that’s all that matters?

At the end of the day, even the Ipopt one isn’t too slow. 75 seconds? We’ve spent much more time than that discussing alternative formulations!

This suggestion reduces it to 62 s. Thanks!

1 Like

Any other optimizer that it is worth taking a look at aside from Ipopt?

Not really. It seems that if you want to keep the outer problem simple, maybe stick with NLopt. And if you want to add more complexity to the outer problem, use Ipopt.