# IPOPT suppress output file generation

**URL:** <https://discourse.julialang.org/t/ipopt-suppress-output-file-generation/127595>\
**Category:** Optimization (Mathematical)\
**Created:** [April 1, 2025, 1:09pm UTC](https://discourse.julialang.org/t/ipopt-suppress-output-file-generation/127595 "2025-04-01T13:09:57Z")\
**Posts on this page:** 1\
**Showing post:** 5

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [April 2, 2025, 8:46pm UTC](https://discourse.julialang.org/t/ipopt-suppress-output-file-generation/127595/5 "2025-04-02T20:46:27Z")

</div>

I took a deeper look. I can’t reproduce this, even with pure Ipopt. Consider:

```julia
julia> using Ipopt

julia> function eval_f(x::Vector{Float64})
           return x[1] * x[4] * (x[1] + x[2] + x[3]) + x[3]
       end
eval_f (generic function with 1 method)

julia> function eval_g(x::Vector{Float64}, g::Vector{Float64})
           g[1] = x[1] * x[2] * x[3] * x[4]
           g[2] = x[1]^2 + x[2]^2 + x[3]^2 + x[4]^2
           return
       end
eval_g (generic function with 1 method)

julia> function eval_grad_f(x::Vector{Float64}, grad_f::Vector{Float64})
           grad_f[1] = x[1] * x[4] + x[4] * (x[1] + x[2] + x[3])
           grad_f[2] = x[1] * x[4]
           grad_f[3] = x[1] * x[4] + 1
           grad_f[4] = x[1] * (x[1] + x[2] + x[3])
           return
       end
eval_grad_f (generic function with 1 method)

julia> function eval_jac_g(
           x::Vector{Float64},
           rows::Vector{Int32},
           cols::Vector{Int32},
           values::Union{Nothing,Vector{Float64}},
       )
           if values === nothing
               rows .= [1, 1, 1, 1, 2, 2, 2, 2]
               cols .= [1, 2, 3, 4, 1, 2, 3, 4]
           else
               values[1] = x[2] * x[3] * x[4] # 1,1
               values[2] = x[1] * x[3] * x[4] # 1,2
               values[3] = x[1] * x[2] * x[4] # 1,3
               values[4] = x[1] * x[2] * x[3] # 1,4
               values[5] = 2 * x[1] # 2,1
               values[6] = 2 * x[2] # 2,2
               values[7] = 2 * x[3] # 2,3
               values[8] = 2 * x[4] # 2,4
           end
           return
       end
eval_jac_g (generic function with 1 method)

julia> function build_problem()
           prob = Ipopt.CreateIpoptProblem(
               4, # n
               [1.0, 1.0, 1.0, 1.0], # x_L
               [5.0, 5.0, 5.0, 5.0], # x_U
               2, # m
               [25.0, 40.0], # g_L
               [Inf, 40.0], # g_U
               8,
               10,
               eval_f,
               eval_g,
               eval_grad_f,
               eval_jac_g,
               nothing, # eval_h,
           )
           Ipopt.AddIpoptStrOption(prob, "hessian_approximation", "limited-memory")
           return prob
       end
build_problem (generic function with 1 method)

julia> p1 = build_problem();

julia> Ipopt.OpenIpoptOutputFile(p1, "ipopt.log", 5)

julia> p2 = build_problem();

julia> p2.x = [1.0, 4.75, 3.82, 1.379];

julia> Ipopt.OpenIpoptOutputFile(p2, "ipopt.log", 5)

julia> Ipopt.IpoptSolve(p2)
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.

Number of nonzeros in equality constraint Jacobian...: 4
Number of nonzeros in inequality constraint Jacobian.: 4
Number of nonzeros in Lagrangian Hessian.............: 0

Total number of variables............................: 4
                     variables with only lower bounds: 0
                variables with lower and upper bounds: 4
                     variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 1
        inequality constraints with only lower bounds: 1
   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 1.7162928e+01 7.66e-02 5.20e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
   1 1.7103404e+01 1.81e-04 1.46e-02 -1.8 1.35e-01 - 9.91e-01 1.00e+00h 1
   2 1.7015921e+01 1.73e-04 3.11e-02 -3.1 1.36e-01 - 1.00e+00 9.99e-01f 1
   3 1.7014053e+01 5.93e-05 1.10e-02 -5.0 2.58e-03 - 1.00e+00 1.00e+00h 1
   4 1.7014006e+01 1.99e-05 4.20e-03 -6.4 3.53e-03 - 1.00e+00 9.95e-01h 1
   5 1.7014016e+01 1.43e-06 1.01e-05 -8.5 9.44e-04 - 1.00e+00 1.00e+00h 1
   6 1.7014017e+01 4.97e-14 2.07e-08 -11.0 1.49e-07 - 1.00e+00 1.00e+00h 1
   7 1.7014017e+01 0.00e+00 1.75e-12 -11.0 5.27e-11 - 1.00e+00 1.00e+00h 1

Number of Iterations....: 7

                                   (scaled) (unscaled)
Objective...............: 1.7014017140224173e+01 1.7014017140224173e+01
Dual infeasibility......: 1.7487404428810206e-12 1.7487404428810206e-12
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Variable bound violation: 9.9908077366706038e-09 9.9908077366706038e-09
Complementarity.........: 1.0000053295879501e-11 1.0000053295879501e-11
Overall NLP error.......: 1.0000053295879501e-11 1.0000053295879501e-11

Number of objective function evaluations = 8
Number of objective gradient evaluations = 8
Number of equality constraint evaluations = 8
Number of inequality constraint evaluations = 8
Number of equality constraint Jacobian evaluations = 8
Number of inequality constraint Jacobian evaluations = 8
Number of Lagrangian Hessian evaluations = 0
Total seconds in IPOPT = 0.073

EXIT: Optimal Solution Found.
0

julia> print(read("ipopt.log", String))
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.

Number of nonzeros in equality constraint Jacobian...: 4
Number of nonzeros in inequality constraint Jacobian.: 4
Number of nonzeros in Lagrangian Hessian.............: 0

Total number of variables............................: 4
                     variables with only lower bounds: 0
                variables with lower and upper bounds: 4
                     variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 1
        inequality constraints with only lower bounds: 1
   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 1.7162928e+01 7.66e-02 5.20e-01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
   1 1.7103404e+01 1.81e-04 1.46e-02 -1.8 1.35e-01 - 9.91e-01 1.00e+00h 1
   2 1.7015921e+01 1.73e-04 3.11e-02 -3.1 1.36e-01 - 1.00e+00 9.99e-01f 1
   3 1.7014053e+01 5.93e-05 1.10e-02 -5.0 2.58e-03 - 1.00e+00 1.00e+00h 1
   4 1.7014006e+01 1.99e-05 4.20e-03 -6.4 3.53e-03 - 1.00e+00 9.95e-01h 1
   5 1.7014016e+01 1.43e-06 1.01e-05 -8.5 9.44e-04 - 1.00e+00 1.00e+00h 1
   6 1.7014017e+01 4.97e-14 2.07e-08 -11.0 1.49e-07 - 1.00e+00 1.00e+00h 1
   7 1.7014017e+01 0.00e+00 1.75e-12 -11.0 5.27e-11 - 1.00e+00 1.00e+00h 1

Number of Iterations....: 7

                                   (scaled) (unscaled)
Objective...............: 1.7014017140224173e+01 1.7014017140224173e+01
Dual infeasibility......: 1.7487404428810206e-12 1.7487404428810206e-12
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Variable bound violation: 9.9908077366706038e-09 9.9908077366706038e-09
Complementarity.........: 1.0000053295879501e-11 1.0000053295879501e-11
Overall NLP error.......: 1.0000053295879501e-11 1.0000053295879501e-11

Number of objective function evaluations = 8
Number of objective gradient evaluations = 8
Number of equality constraint evaluations = 8
Number of inequality constraint evaluations = 8
Number of equality constraint Jacobian evaluations = 8
Number of inequality constraint Jacobian evaluations = 8
Number of Lagrangian Hessian evaluations = 0
Total seconds in IPOPT = 0.073

EXIT: Optimal Solution Found.

julia> Ipopt.IpoptSolve(p1)
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.

Number of nonzeros in equality constraint Jacobian...: 4
Number of nonzeros in inequality constraint Jacobian.: 4
Number of nonzeros in Lagrangian Hessian.............: 0

Total number of variables............................: 4
                     variables with only lower bounds: 0
                variables with lower and upper bounds: 4
                     variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 1
        inequality constraints with only lower bounds: 1
   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 4.1009029e+00 3.59e+01 1.54e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
   1 6.3369396e+00 3.43e+01 2.40e+01 -5.7 5.89e+00 - 2.21e-03 4.20e-02h 1
   2 5.4097700e+00 3.42e+01 1.38e+02 0.2 5.15e+00 - 1.72e-05 2.32e-03F 1
   3 3.9539430e+01 1.38e+01 1.45e+03 -1.1 4.74e+00 - 1.00e+00 3.76e-01h 2
   4 1.8118029e+01 2.90e+00 7.27e+02 0.7 2.49e+01 - 1.00e+00 6.47e-01f 1
   5 2.0775371e+01 1.57e-01 8.47e-01 0.1 1.12e+01 - 1.00e+00 1.00e+00f 1
   6 1.7334617e+01 1.78e-01 3.19e-01 -1.6 5.17e+00 - 9.23e-01 1.00e+00f 1
   7 1.6866329e+01 3.04e-01 1.62e-01 -1.6 3.10e-01 - 1.00e+00 1.00e+00h 1
   8 1.6989287e+01 4.78e-02 3.79e-02 -2.4 1.30e-01 - 9.95e-01 1.00e+00h 1
   9 1.7013532e+01 1.25e-03 6.00e-03 -3.7 1.35e-02 - 9.96e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
  10 1.7014026e+01 5.99e-07 5.62e-04 -5.3 4.51e-04 - 1.00e+00 1.00e+00h 1
  11 1.7014017e+01 5.43e-08 3.78e-06 -7.4 1.84e-04 - 1.00e+00 1.00e+00h 1
  12 1.7014017e+01 1.56e-11 3.32e-09 -11.0 3.12e-06 - 1.00e+00 1.00e+00h 1

Number of Iterations....: 12

                                   (scaled) (unscaled)
Objective...............: 1.7014017140214442e+01 1.7014017140214442e+01
Dual infeasibility......: 3.3177681309590336e-09 3.3177681309590336e-09
Constraint violation....: 1.5603518477291800e-11 1.5603518477291800e-11
Variable bound violation: 9.9909249762220043e-09 9.9909249762220043e-09
Complementarity.........: 1.0110284647548166e-11 1.0110284647548166e-11
Overall NLP error.......: 3.3177681309590336e-09 3.3177681309590336e-09

Number of objective function evaluations = 17
Number of objective gradient evaluations = 13
Number of equality constraint evaluations = 17
Number of inequality constraint evaluations = 17
Number of equality constraint Jacobian evaluations = 13
Number of inequality constraint Jacobian evaluations = 13
Number of Lagrangian Hessian evaluations = 0
Total seconds in IPOPT = 0.014

EXIT: Optimal Solution Found.
0

julia> print(read("ipopt.log", String))
This is Ipopt version 3.14.14, running with linear solver MUMPS 5.6.2.

Number of nonzeros in equality constraint Jacobian...: 4
Number of nonzeros in inequality constraint Jacobian.: 4
Number of nonzeros in Lagrangian Hessian.............: 0

Total number of variables............................: 4
                     variables with only lower bounds: 0
                variables with lower and upper bounds: 4
                     variables with only upper bounds: 0
Total number of equality constraints.................: 1
Total number of inequality constraints...............: 1
        inequality constraints with only lower bounds: 1
   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 4.1009029e+00 3.59e+01 1.54e+00 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
   1 6.3369396e+00 3.43e+01 2.40e+01 -5.7 5.89e+00 - 2.21e-03 4.20e-02h 1
   2 5.4097700e+00 3.42e+01 1.38e+02 0.2 5.15e+00 - 1.72e-05 2.32e-03F 1
   3 3.9539430e+01 1.38e+01 1.45e+03 -1.1 4.74e+00 - 1.00e+00 3.76e-01h 2
   4 1.8118029e+01 2.90e+00 7.27e+02 0.7 2.49e+01 - 1.00e+00 6.47e-01f 1
   5 2.0775371e+01 1.57e-01 8.47e-01 0.1 1.12e+01 - 1.00e+00 1.00e+00f 1
   6 1.7334617e+01 1.78e-01 3.19e-01 -1.6 5.17e+00 - 9.23e-01 1.00e+00f 1
   7 1.6866329e+01 3.04e-01 1.62e-01 -1.6 3.10e-01 - 1.00e+00 1.00e+00h 1
   8 1.6989287e+01 4.78e-02 3.79e-02 -2.4 1.30e-01 - 9.95e-01 1.00e+00h 1
   9 1.7013532e+01 1.25e-03 6.00e-03 -3.7 1.35e-02 - 9.96e-01 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
  10 1.7014026e+01 5.99e-07 5.62e-04 -5.3 4.51e-04 - 1.00e+00 1.00e+00h 1
  11 1.7014017e+01 5.43e-08 3.78e-06 -7.4 1.84e-04 - 1.00e+00 1.00e+00h 1
  12 1.7014017e+01 1.56e-11 3.32e-09 -11.0 3.12e-06 - 1.00e+00 1.00e+00h 1

Number of Iterations....: 12

                                   (scaled) (unscaled)
Objective...............: 1.7014017140214442e+01 1.7014017140214442e+01
Dual infeasibility......: 3.3177681309590336e-09 3.3177681309590336e-09
Constraint violation....: 1.5603518477291800e-11 1.5603518477291800e-11
Variable bound violation: 9.9909249762220043e-09 9.9909249762220043e-09
Complementarity.........: 1.0110284647548166e-11 1.0110284647548166e-11
Overall NLP error.......: 3.3177681309590336e-09 3.3177681309590336e-09

Number of objective function evaluations = 17
Number of objective gradient evaluations = 13
Number of equality constraint evaluations = 17
Number of inequality constraint evaluations = 17
Number of equality constraint Jacobian evaluations = 13
Number of inequality constraint Jacobian evaluations = 13
Number of Lagrangian Hessian evaluations = 0
Total seconds in IPOPT = 0.014

EXIT: Optimal Solution Found.

```

`p1` opens the file first, then `p2` opens it, solves and writes to file, then when `p1` solves it overwrites the existing file. That seems to be working as expected?

---

_[View the full topic](https://discourse.julialang.org/t/ipopt-suppress-output-file-generation/127595)._