"bad line xxx of yyy.nl file" error with AmplNLWriter (but solves with Ipopt.jl)

Specific Domains Optimization (Mathematical)
1

The following code solves correctly using the Ipopt.jl Ipopt interface, but returns an error “bad line xxx of yyy.nl file” when I try to use the AmplNLWriter.jl interface (both using Ipopt and Ipopt_jll).
Other “basic” models using the AmplNLWriter interface seem to work fine (so does ] test AmplNLWriter) so I can’t really point out what is the problem. Should I fill a bug report ? In which repo? AmplNLWriter.jl ?

using Pkg
Pkg.activate(temp=true)
Pkg.add(["JuMP","Ipopt","AmplNLWriter","Ipopt_jll"])
using JuMP
using Ipopt
using AmplNLWriter, Ipopt_jll

function solveEquilibriumWithoutTrade(
    ampl = false,
    const_d = [50.0 100.0; 100.0 10.0; 200.0 200.0; 200.0 200.0; 40.0 10.0],
    ϵ_d = [-0.8 0.4; 0.3 0.15; 0.0 0.0; 0.0 0.0; 0.0 0.0;;; 0.3 0.15; -0.8 0.4; 0.0 0.0; 0.0 0.0; 0.0 0.0;;; 0.6 0.3; 0.0 0.0; -0.8 0.4; 0.4 0.2; 0.4 0.2;;; 0.0 0.0; 0.6 0.15; 0.4 0.2; -0.8 0.4; 0.4 0.2;;; 0.3 0.15; 0.3 0.15; 0.4 0.2; 0.4 0.2; -0.8 0.4;;;; 0.4 -0.8; 0.15 0.3; 0.0 0.0; 0.0 0.0; 0.0 0.0;;; 0.15 0.3; 0.4 -0.8; 0.0 0.0; 0.0 0.0; 0.0 0.0;;; 0.3 0.6; 0.0 0.0; 0.4 -0.8; 0.2 0.4; 0.2 0.4;;; 0.0 0.0; 0.15 0.6; 0.2 0.4; 0.4 -0.8; 0.2 0.4;;; 0.15 0.3; 0.15 0.3; 0.2 0.4; 0.2 0.4; 0.4 -0.8],
    d0 = [72.0 100.0; 100.0 100.0; 50.0 50.0; 50.0 50.0; 10.0 10.0],
    const_s = [100.0 100.0; 100.0 100.0; 200.0 200.0; 200.0 200.0; 10.0 10.0],
    ϵ_s = [0.8 0.0; 0.0 0.0; -0.6 -0.3; 0.0 0.0; -0.6 -0.3;;; 0.0 0.0; 0.8 0.0; 0.0 0.0; -0.6 -0.3; -0.6 -0.3;;; 0.0 0.0; 0.0 0.0; 0.8 0.0; 0.0 0.0; 0.0 0.0;;; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.8 0.0; 0.0 0.0;;; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.8 0.0;;;; 0.0 0.8; 0.0 0.0; -0.3 -0.6; 0.0 0.0; -0.3 -0.6;;; 0.0 0.0; 0.0 0.0; 0.0 0.0; -0.3 -0.6; -0.3 -0.6;;; 0.0 0.0; 0.0 0.0; 0.0 0.8; 0.0 0.0; 0.0 0.0;;; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.8; 0.0 0.0;;; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.8],
    s0 = [72.0 100.0; 100.0 100.0; 50.0 50.0; 50.0 50.0; 10.0 10.0],
    pr0 = [0.64 10.0; 10.0 10.0; 30.0 30.0; 30.0 30.0; 100.0 100.0];
    )
    
    (np, nr) = size(ϵ_d)
  
    # *** Model declaration ***
    if(ampl)
        m = Model(() -> AmplNLWriter.Optimizer(Ipopt_jll.amplexe))
    else
        m = Model(optimizer_with_attributes(Ipopt.Optimizer))
    end
  
  
    # *** Variables declaration ***
    @variable(m, d[p = 1:np, r = 1:nr] >= 0,  start=d0[p,r])   # Demand quantity
    @variable(m, s[p = 1:np, r = 1:nr] >= 0,  start=s0[p,r])   # Supply quantity
    @variable(m, pr[p = 1:np, r = 1:nr] >= 0,  start=pr0[p,r]) # Price
  
    # *** Constraints declaration (and definition) ***
    @NLconstraint(m, demand[p in 1:np, r in 1:nr],  d[p,r]  == const_d[p,r] * prod(pr[in_p,in_r]^ ϵ_d[p,r,in_p,in_r] for in_p in 1:np, in_r in 1:nr if ϵ_d[p,r,in_p,in_r] != 0.0  ) )
    @NLconstraint(m, supply[p in 1:np, r in 1:nr],  s[p,r]  == const_s[p,r] * prod(pr[in_p,in_r]^ ϵ_s[p,r,in_p,in_r] for in_p in 1:np, in_r in 1:nr if ϵ_s[p,r,in_p,in_r] != 0.0) )
  
    # *** Objective function ***
    @NLobjective(m, Min,
        sum((d[p,r] - s[p,r])^2 for p in 1:np, r in 1:nr)
    )
  
    # *** Print human-readable version of the model ***
    print(m)
  
    # *** Solve the model  ***
    optimize!(m)
    status = termination_status(m)
  

  end

solveEquilibriumWithoutTrade()
solveEquilibriumWithoutTrade(true)
Output 
julia> 

Min (d[1,1] - s[1,1]) ^ 2.0 + (d[1,2] - s[1,2]) ^ 2.0 + (d[2,1] - s[2,1]) ^ 2.0 + (d[2,2] - s[2,2]) ^ 2.0 + (d[3,1] - s[3,1]) ^ 2.0 + (d[3,2] - s[3,2]) ^ 2.0 + (d[4,1] - s[4,1]) ^ 2.0 + (d[4,2] - s[4,2]) ^ 2.0 + (d[5,1] - s[5,1]) ^ 2.0 + (d[5,2] - s[5,2]) ^ 2.0
Subject to
 d[1,1] ≥ 0.0
 d[2,1] ≥ 0.0
 d[3,1] ≥ 0.0
 d[4,1] ≥ 0.0
 d[5,1] ≥ 0.0
 d[1,2] ≥ 0.0
 d[2,2] ≥ 0.0
 d[3,2] ≥ 0.0
 d[4,2] ≥ 0.0
 d[5,2] ≥ 0.0
 s[1,1] ≥ 0.0
 s[2,1] ≥ 0.0
 s[3,1] ≥ 0.0
 s[4,1] ≥ 0.0
 s[5,1] ≥ 0.0
 s[1,2] ≥ 0.0
 s[2,2] ≥ 0.0
 s[3,2] ≥ 0.0
 s[4,2] ≥ 0.0
 s[5,2] ≥ 0.0
 pr[1,1] ≥ 0.0
 pr[2,1] ≥ 0.0
 pr[3,1] ≥ 0.0
 pr[4,1] ≥ 0.0
 pr[5,1] ≥ 0.0
 pr[1,2] ≥ 0.0
 pr[2,2] ≥ 0.0
 pr[3,2] ≥ 0.0
 pr[4,2] ≥ 0.0
 pr[5,2] ≥ 0.0
 d[1,1] - 50.0 * (pr[1,1] ^ -0.8 * pr[1,2] ^ 0.4 * pr[2,1] ^ 0.3 * pr[2,2] ^ 0.15 * pr[3,1] ^ 0.6 * pr[3,2] ^ 0.3 * pr[5,1] ^ 0.3 * pr[5,2] ^ 0.15) = 0
 d[2,1] - 100.0 * (pr[1,1] ^ 0.3 * pr[1,2] ^ 0.15 * pr[2,1] ^ -0.8 * pr[2,2] ^ 0.4 * pr[4,1] ^ 0.6 * pr[4,2] ^ 0.15 * pr[5,1] ^ 0.3 * pr[5,2] ^ 0.15) = 0
 d[3,1] - 200.0 * (pr[3,1] ^ -0.8 * pr[3,2] ^ 0.4 * pr[4,1] ^ 0.4 * pr[4,2] ^ 0.2 * pr[5,1] ^ 0.4 * pr[5,2] ^ 0.2) = 0
 d[4,1] - 200.0 * (pr[3,1] ^ 0.4 * pr[3,2] ^ 0.2 * pr[4,1] ^ -0.8 * pr[4,2] ^ 0.4 * pr[5,1] ^ 0.4 * pr[5,2] ^ 0.2) = 0
 d[5,1] - 40.0 * (pr[3,1] ^ 0.4 * pr[3,2] ^ 0.2 * pr[4,1] ^ 0.4 * pr[4,2] ^ 0.2 * pr[5,1] ^ -0.8 * pr[5,2] ^ 0.4) = 0
 d[1,2] - 100.0 * (pr[1,1] ^ 0.4 * pr[1,2] ^ -0.8 * pr[2,1] ^ 0.15 * pr[2,2] ^ 0.3 * pr[3,1] ^ 0.3 * pr[3,2] ^ 0.6 * pr[5,1] ^ 0.15 * pr[5,2] ^ 0.3) = 0
 d[2,2] - 10.0 * (pr[1,1] ^ 0.15 * pr[1,2] ^ 0.3 * pr[2,1] ^ 0.4 * pr[2,2] ^ -0.8 * pr[4,1] ^ 0.15 * pr[4,2] ^ 0.6 * pr[5,1] ^ 0.15 * pr[5,2] ^ 0.3) = 0
 d[3,2] - 200.0 * (pr[3,1] ^ 0.4 * pr[3,2] ^ -0.8 * pr[4,1] ^ 0.2 * pr[4,2] ^ 0.4 * pr[5,1] ^ 0.2 * pr[5,2] ^ 0.4) = 0
 d[4,2] - 200.0 * (pr[3,1] ^ 0.2 * pr[3,2] ^ 0.4 * pr[4,1] ^ 0.4 * pr[4,2] ^ -0.8 * pr[5,1] ^ 0.2 * pr[5,2] ^ 0.4) = 0
 d[5,2] - 10.0 * (pr[3,1] ^ 0.2 * pr[3,2] ^ 0.4 * pr[4,1] ^ 0.2 * pr[4,2] ^ 0.4 * pr[5,1] ^ 0.4 * pr[5,2] ^ -0.8) = 0
 s[1,1] - 100.0 * (*)(pr[1,1] ^ 0.8) = 0
 s[2,1] - 100.0 * (*)(pr[2,1] ^ 0.8) = 0
 s[3,1] - 200.0 * (pr[1,1] ^ -0.6 * pr[1,2] ^ -0.3 * pr[3,1] ^ 0.8) = 0
 s[4,1] - 200.0 * (pr[2,1] ^ -0.6 * pr[2,2] ^ -0.3 * pr[4,1] ^ 0.8) = 0
 s[5,1] - 10.0 * (pr[1,1] ^ -0.6 * pr[1,2] ^ -0.3 * pr[2,1] ^ -0.6 * pr[2,2] ^ -0.3 * pr[5,1] ^ 0.8) = 0
 s[1,2] - 100.0 * (*)(pr[1,2] ^ 0.8) = 0
 s[2,2] - 100.0 * 1 = 0
 s[3,2] - 200.0 * (pr[1,1] ^ -0.3 * pr[1,2] ^ -0.6 * pr[3,2] ^ 0.8) = 0
 s[4,2] - 200.0 * (pr[2,1] ^ -0.3 * pr[2,2] ^ -0.6 * pr[4,2] ^ 0.8) = 0
 s[5,2] - 10.0 * (pr[1,1] ^ -0.3 * pr[1,2] ^ -0.6 * pr[2,1] ^ -0.3 * pr[2,2] ^ -0.6 * pr[5,2] ^ 0.8) = 0
This is Ipopt version 3.14.4, running with linear solver MUMPS 5.4.1.

Number of nonzeros in equality constraint Jacobian...:      113
Number of nonzeros in inequality constraint Jacobian.:        0
Number of nonzeros in Lagrangian Hessian.............:      357

Total number of variables............................:       30
                     variables with only lower bounds:       30
                variables with lower and upper bounds:        0
                     variables with only upper bounds:        0
Total number of equality constraints.................:       20
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  0.0000000e+00 8.57e+04 1.64e+00  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
   1  5.6883018e+03 5.77e+04 1.49e+03  -1.0 8.33e+02    -  4.65e-02 3.06e-01h  1
   2  8.5432068e+03 7.69e+04 9.37e+05  -1.0 3.00e+03    -  6.71e-03 4.61e-01h  1
   3  1.8415793e+03 6.39e+03 1.07e+05  -1.0 1.60e+03    -  2.43e-02 1.00e+00f  1
   4  1.5214364e+03 3.23e+03 2.28e+06  -1.0 2.83e+03    -  4.51e-01 7.19e-01f  1
   5  3.4340351e+04 3.09e+03 1.25e+06  -1.0 1.56e+03    -  6.12e-02 7.59e-02H  1
   6  3.3455172e+04 2.93e+03 1.20e+06  -1.0 1.04e+03  -2.0 9.15e-02 2.31e-02F  1
   7  5.7251670e+04 6.90e+02 2.06e+05  -1.0 3.20e+03    -  3.53e-02 2.15e-01h  1
   8  4.3274463e+04 5.90e+02 2.71e+05  -1.0 3.45e+02    -  7.10e-03 1.40e-01f  1
   9  4.2736841e+04 5.87e+02 2.69e+05  -1.0 4.17e+02    -  1.27e-02 6.07e-03f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  10  4.7888081e+04 2.12e+02 4.11e+05  -1.0 5.26e+02    -  7.17e-03 9.70e-02H  1
  11  4.7387800e+04 2.35e+02 3.99e+05  -1.0 3.22e+03    -  3.43e-04 1.18e-02f  3
  12  5.7495079e+04 2.02e+02 3.63e+05  -1.0 1.92e+02   0.2 1.13e-01 1.07e-01H  1
  13  6.3041232e+04 1.56e+02 3.22e+05  -1.0 3.15e+02  -0.2 8.73e-05 2.48e-01h  2
  14  8.8386892e+04 1.52e+02 3.03e+05  -1.0 4.85e+02   0.2 1.02e-01 7.14e-02h  3
  15  2.8374415e+05 2.53e+02 2.81e+05  -1.0 1.04e+02   0.6 2.38e-01 5.59e-01H  1
  16  2.5168364e+05 3.29e+02 3.65e+06  -1.0 1.19e+02   1.0 9.02e-02 1.00e+00f  1
  17  1.8480724e+05 2.03e+02 2.37e+06  -1.0 5.13e+01   1.5 6.57e-01 1.00e+00f  1
  18  1.4235193e+05 2.31e+02 2.25e+06  -1.0 2.20e+03   1.0 5.26e-02 4.71e-02f  2
  19  1.3903370e+05 2.36e+02 2.19e+06  -1.0 9.13e+03   1.4 1.81e-02 7.47e-03f  2
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  20  1.4073316e+05 2.27e+02 2.10e+06  -1.0 3.75e+02   1.8 4.45e-01 4.59e-02h  3
  21  1.7651054e+05 1.10e+02 2.27e+06  -1.0 7.74e+01   2.3 1.00e+00 1.00e+00h  1
  22  1.8668105e+05 2.33e+01 3.24e+06  -1.0 3.58e+01   2.7 6.18e-01 1.00e+00h  1
  23  1.7740758e+05 6.86e-01 3.11e+05  -1.0 1.68e+01   2.2 1.00e+00 1.00e+00f  1
  24  1.5983850e+05 2.08e-01 2.86e+04  -1.0 9.56e+00   1.7 1.00e+00 1.00e+00f  1
  25  1.1768725e+05 1.62e+00 1.19e+03  -1.0 2.60e+01   1.3 1.00e+00 1.00e+00f  1
  26  5.5212240e+04 9.08e+00 5.48e+03  -1.0 5.75e+01   0.8 1.00e+00 1.00e+00f  1
  27  1.3433566e+04 2.38e+01 5.40e+03  -1.0 8.27e+01   0.3 1.00e+00 1.00e+00f  1
  28  9.0730301e+03 7.58e+01 4.79e+03  -1.0 1.14e+03    -  1.53e-01 9.70e-02f  2
  29  1.4996331e+02 5.41e+01 7.44e+03  -1.0 1.69e+02    -  1.00e+00 1.00e+00f  1
iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
  30  6.7247379e+01 3.11e+01 7.46e+02  -1.0 3.68e+01    -  1.00e+00 1.00e+00f  1
  31  1.7651371e-01 2.21e-01 7.11e+01  -1.0 9.40e+00    -  7.13e-01 1.00e+00f  1
  32  2.7549528e-05 5.44e-03 5.38e-01  -1.0 2.08e-01    -  1.00e+00 1.00e+00h  1
  33  9.0578900e-07 3.71e-07 1.01e-04  -1.7 5.58e-03    -  1.00e+00 1.00e+00h  1
  34  5.1314470e-11 6.07e-09 3.72e-06  -3.8 1.81e-03    -  1.00e+00 1.00e+00h  1
  35  7.7065711e-15 3.41e-13 2.27e-10  -5.7 1.36e-05    -  1.00e+00 1.00e+00h  1
  36  1.4218476e-20 3.41e-13 9.56e-14  -8.6 1.68e-07    -  1.00e+00 1.00e+00h  1

Number of Iterations....: 36

                                   (scaled)                 (unscaled)
Objective...............:   1.4218475719377978e-20    1.4218475719377978e-20
Dual infeasibility......:   9.5634958457802712e-14    9.5634958457802712e-14
Constraint violation....:   2.0436591416786226e-13    3.4106051316484809e-13
Variable bound violation:   0.0000000000000000e+00    0.0000000000000000e+00
Complementarity.........:   2.5059052064168116e-09    2.5059052064168116e-09
Overall NLP error.......:   2.5059052064168116e-09    2.5059052064168116e-09


Number of objective function evaluations             = 69
Number of objective gradient evaluations             = 37
Number of equality constraint evaluations            = 69
Number of inequality constraint evaluations          = 0
Number of equality constraint Jacobian evaluations   = 37
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations             = 36
Total seconds in IPOPT                               = 0.071

EXIT: Optimal Solution Found.

Min (d[1,1] - s[1,1]) ^ 2.0 + (d[1,2] - s[1,2]) ^ 2.0 + (d[2,1] - s[2,1]) ^ 2.0 + (d[2,2] - s[2,2]) ^ 2.0 + (d[3,1] - s[3,1]) ^ 2.0 + (d[3,2] - s[3,2]) ^ 2.0 + (d[4,1] - s[4,1]) ^ 2.0 + (d[4,2] - s[4,2]) ^ 2.0 + (d[5,1] - s[5,1]) ^ 2.0 + (d[5,2] - s[5,2]) ^ 2.0
Subject to
 d[1,1] ≥ 0.0
 d[2,1] ≥ 0.0
 d[3,1] ≥ 0.0
 d[4,1] ≥ 0.0
 d[5,1] ≥ 0.0
 d[1,2] ≥ 0.0
 d[2,2] ≥ 0.0
 d[3,2] ≥ 0.0
 d[4,2] ≥ 0.0
 d[5,2] ≥ 0.0
 s[1,1] ≥ 0.0
 s[2,1] ≥ 0.0
 s[3,1] ≥ 0.0
 s[4,1] ≥ 0.0
 s[5,1] ≥ 0.0
 s[1,2] ≥ 0.0
 s[2,2] ≥ 0.0
 s[3,2] ≥ 0.0
 s[4,2] ≥ 0.0
 s[5,2] ≥ 0.0
 pr[1,1] ≥ 0.0
 pr[2,1] ≥ 0.0
 pr[3,1] ≥ 0.0
 pr[4,1] ≥ 0.0
 pr[5,1] ≥ 0.0
 pr[1,2] ≥ 0.0
 pr[2,2] ≥ 0.0
 pr[3,2] ≥ 0.0
 pr[4,2] ≥ 0.0
 pr[5,2] ≥ 0.0
 d[1,1] - 50.0 * (pr[1,1] ^ -0.8 * pr[1,2] ^ 0.4 * pr[2,1] ^ 0.3 * pr[2,2] ^ 0.15 * pr[3,1] ^ 0.6 * pr[3,2] ^ 0.3 * pr[5,1] ^ 0.3 * pr[5,2] ^ 0.15) = 0
 d[2,1] - 100.0 * (pr[1,1] ^ 0.3 * pr[1,2] ^ 0.15 * pr[2,1] ^ -0.8 * pr[2,2] ^ 0.4 * pr[4,1] ^ 0.6 * pr[4,2] ^ 0.15 * pr[5,1] ^ 0.3 * pr[5,2] ^ 0.15) = 0
 d[3,1] - 200.0 * (pr[3,1] ^ -0.8 * pr[3,2] ^ 0.4 * pr[4,1] ^ 0.4 * pr[4,2] ^ 0.2 * pr[5,1] ^ 0.4 * pr[5,2] ^ 0.2) = 0
 d[4,1] - 200.0 * (pr[3,1] ^ 0.4 * pr[3,2] ^ 0.2 * pr[4,1] ^ -0.8 * pr[4,2] ^ 0.4 * pr[5,1] ^ 0.4 * pr[5,2] ^ 0.2) = 0
 d[5,1] - 40.0 * (pr[3,1] ^ 0.4 * pr[3,2] ^ 0.2 * pr[4,1] ^ 0.4 * pr[4,2] ^ 0.2 * pr[5,1] ^ -0.8 * pr[5,2] ^ 0.4) = 0
 d[1,2] - 100.0 * (pr[1,1] ^ 0.4 * pr[1,2] ^ -0.8 * pr[2,1] ^ 0.15 * pr[2,2] ^ 0.3 * pr[3,1] ^ 0.3 * pr[3,2] ^ 0.6 * pr[5,1] ^ 0.15 * pr[5,2] ^ 0.3) = 0
 d[2,2] - 10.0 * (pr[1,1] ^ 0.15 * pr[1,2] ^ 0.3 * pr[2,1] ^ 0.4 * pr[2,2] ^ -0.8 * pr[4,1] ^ 0.15 * pr[4,2] ^ 0.6 * pr[5,1] ^ 0.15 * pr[5,2] ^ 0.3) = 0
 d[3,2] - 200.0 * (pr[3,1] ^ 0.4 * pr[3,2] ^ -0.8 * pr[4,1] ^ 0.2 * pr[4,2] ^ 0.4 * pr[5,1] ^ 0.2 * pr[5,2] ^ 0.4) = 0
 d[4,2] - 200.0 * (pr[3,1] ^ 0.2 * pr[3,2] ^ 0.4 * pr[4,1] ^ 0.4 * pr[4,2] ^ -0.8 * pr[5,1] ^ 0.2 * pr[5,2] ^ 0.4) = 0
 d[5,2] - 10.0 * (pr[3,1] ^ 0.2 * pr[3,2] ^ 0.4 * pr[4,1] ^ 0.2 * pr[4,2] ^ 0.4 * pr[5,1] ^ 0.4 * pr[5,2] ^ -0.8) = 0
 s[1,1] - 100.0 * (*)(pr[1,1] ^ 0.8) = 0
 s[2,1] - 100.0 * (*)(pr[2,1] ^ 0.8) = 0
 s[3,1] - 200.0 * (pr[1,1] ^ -0.6 * pr[1,2] ^ -0.3 * pr[3,1] ^ 0.8) = 0
 s[4,1] - 200.0 * (pr[2,1] ^ -0.6 * pr[2,2] ^ -0.3 * pr[4,1] ^ 0.8) = 0
 s[5,1] - 10.0 * (pr[1,1] ^ -0.6 * pr[1,2] ^ -0.3 * pr[2,1] ^ -0.6 * pr[2,2] ^ -0.3 * pr[5,1] ^ 0.8) = 0
 s[1,2] - 100.0 * (*)(pr[1,2] ^ 0.8) = 0
 s[2,2] - 100.0 * 1 = 0
 s[3,2] - 200.0 * (pr[1,1] ^ -0.3 * pr[1,2] ^ -0.6 * pr[3,2] ^ 0.8) = 0
 s[4,2] - 200.0 * (pr[2,1] ^ -0.3 * pr[2,2] ^ -0.6 * pr[4,2] ^ 0.8) = 0
 s[5,2] - 10.0 * (pr[1,1] ^ -0.3 * pr[1,2] ^ -0.6 * pr[2,1] ^ -0.3 * pr[2,2] ^ -0.6 * pr[5,2] ^ 0.8) = 0
Ipopt 3.14.4: 
bad line 332 of /tmp/jl_z6qvlg/model.nl: C1
Cannot open .nl file
terminate called after throwing an instance of 'Ipopt::TNLP::INVALID_TNLP'
The content of /tmp/jl_z6qvlg/model.nl: ``` g3 1 1 0 30 20 1 0 20 0 20 1 0 0 30 20 20 0 0 0 1 0 0 0 0 0 113 20 0 0 0 0 0 0 0 C0 o1 v0 o2 n50 o2 o5 v20 n-0.8 o2 o5 v25 n0.4 o2 o5 v21 n0.3 o2 o5 v26 n0.15 o2 o5 v22 n0.6 o2 o5 v27 n0.3 o2 o5 v24 n0.3 o5 v29 n0.15 C1 o1 v1 o2 n100 o2 o5 v20 n0.3 o2 o5 v25 n0.15 o2 o5 v21 n-0.8 o2 o5 v26 n0.4 o2 o5 v23 n0.6 o2 o5 v28 n0.15 o2 o5 v24 n0.3 o5 v29 n0.15 C2 o1 v2 o2 n200 o2 o5 v22 n-0.8 o2 o5 v27 n0.4 o2 o5 v23 n0.4 o2 o5 v28 n0.2 o2 o5 v24 n0.4 o5 v29 n0.2 C3 o1 v3 o2 n200 o2 o5 v22 n0.4 o2 o5 v27 n0.2 o2 o5 v23 n-0.8 o2 o5 v28 n0.4 o2 o5 v24 n0.4 o5 v29 n0.2 C4 o1 v4 o2 n40 o2 o5 v22 n0.4 o2 o5 v27 n0.2 o2 o5 v23 n0.4 o2 o5 v28 n0.2 o2 o5 v24 n-0.8 o5 v29 n0.4 C5 o1 v5 o2 n100 o2 o5 v20 n0.4 o2 o5 v25 n-0.8 o2 o5 v21 n0.15 o2 o5 v26 n0.3 o2 o5 v22 n0.3 o2 o5 v27 n0.6 o2 o5 v24 n0.15 o5 v29 n0.3 C6 o1 v6 o2 n10 o2 o5 v20 n0.15 o2 o5 v25 n0.3 o2 o5 v21 n0.4 o2 o5 v26 n-0.8 o2 o5 v23 n0.15 o2 o5 v28 n0.6 o2 o5 v24 n0.15 o5 v29 n0.3 C7 o1 v7 o2 n200 o2 o5 v22 n0.4 o2 o5 v27 n-0.8 o2 o5 v23 n0.2 o2 o5 v28 n0.4 o2 o5 v24 n0.2 o5 v29 n0.4 C8 o1 v8 o2 n200 o2 o5 v22 n0.2 o2 o5 v27 n0.4 o2 o5 v23 n0.4 o2 o5 v28 n-0.8 o2 o5 v24 n0.2 o5 v29 n0.4 C9 o1 v9 o2 n10 o2 o5 v22 n0.2 o2 o5 v27 n0.4 o2 o5 v23 n0.2 o2 o5 v28 n0.4 o2 o5 v24 n0.4 o5 v29 n-0.8 C10 o1 v10 o2 n100 o2 o5 v20 n0.8 C11 o1 v11 o2 n100 o2 o5 v21 n0.8 C12 o1 v12 o2 n200 o2 o5 v20 n-0.6 o2 o5 v25 n-0.3 o5 v22 n0.8 C13 o1 v13 o2 n200 o2 o5 v21 n-0.6 o2 o5 v26 n-0.3 o5 v23 n0.8 C14 o1 v14 o2 n10 o2 o5 v20 n-0.6 o2 o5 v25 n-0.3 o2 o5 v21 n-0.6 o2 o5 v26 n-0.3 o5 v24 n0.8 C15 o1 v15 o2 n100 o2 o5 v25 n0.8 C16 o1 v16 o2 n100 n1 C17 o1 v17 o2 n200 o2 o5 v20 n-0.3 o2 o5 v25 n-0.6 o5 v27 n0.8 C18 o1 v18 o2 n200 o2 o5 v21 n-0.3 o2 o5 v26 n-0.6 o5 v28 n0.8 C19 o1 v19 o2 n10 o2 o5 v20 n-0.3 o2 o5 v25 n-0.6 o2 o5 v21 n-0.3 o2 o5 v26 n-0.6 o5 v29 n0.8 O0 0 o54 10 o5 o1 v0 v10 n2 o5 o1 v5 v15 n2 o5 o1 v1 v11 n2 o5 o1 v6 v16 n2 o5 o1 v2 v12 n2 o5 o1 v7 v17 n2 o5 o1 v3 v13 n2 o5 o1 v8 v18 n2 o5 o1 v4 v14 n2 o5 o1 v9 v19 n2 x30 0 72 1 100 2 50 3 50 4 10 5 100 6 100 7 50 8 50 9 10 10 72 11 100 12 50 13 50 14 10 15 100 16 100 17 50 18 50 19 10 20 0.64 21 10 22 30 23 30 24 100 25 10 26 10 27 30 28 30 29 100 r 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 4 0 b 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 k29 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 29 38 47 56 67 76 84 93 102 J0 9 0 0 20 0 21 0 22 0 24 0 25 0 26 0 27 0 29 0 J1 9 1 0 20 0 21 0 23 0 24 0 25 0 26 0 28 0 29 0 J2 7 2 0 22 0 23 0 24 0 27 0 28 0 29 0 J3 7 3 0 22 0 23 0 24 0 27 0 28 0 29 0 J4 7 4 0 22 0 23 0 24 0 27 0 28 0 29 0 J5 9 5 0 20 0 21 0 22 0 24 0 25 0 26 0 27 0 29 0 J6 9 6 0 20 0 21 0 23 0 24 0 25 0 26 0 28 0 29 0 J7 7 7 0 22 0 23 0 24 0 27 0 28 0 29 0 J8 7 8 0 22 0 23 0 24 0 27 0 28 0 29 0 J9 7 9 0 22 0 23 0 24 0 27 0 28 0 29 0 J10 2 10 0 20 0 J11 2 11 0 21 0 J12 4 12 0 20 0 22 0 25 0 J13 4 13 0 21 0 23 0 26 0 J14 6 14 0 20 0 21 0 24 0 25 0 26 0 J15 2 15 0 25 0 J16 1 16 0 J17 4 17 0 20 0 25 0 27 0 J18 4 18 0 21 0 26 0 28 0 J19 6 19 0 20 0 21 0 25 0 26 0 29 0 G0 20 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 10 0 11 0 12 0 13 0 14 0 15 0 16 0 17 0 18 0 19 0 ```
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2

just noticed that the problem is in the conditional over the exogenous parameters…

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3

Let me take a look. It seems like a bug.

1 Like
4

It’s a bug in the MOI.FileFormats.NL: FileFormats.NL does not support univariate OPMULT · Issue #1886 · jump-dev/MathOptInterface.jl · GitHub.

We don’t handle prod with a single element correctly.

Here’s the minimal reproducer:

using JuMP
import AmplNLWriter
import Ipopt_jll

function main()
    model = Model(() -> AmplNLWriter.Optimizer(Ipopt_jll.amplexe))
    @variable(model, x)
    @NLobjective(model, Min, prod(x for j=1:1))
    optimize!(model)
    return
end

main()
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