Set output_file via addOption instead of OpenIpoptOutputFile#18
Open
odow wants to merge 1 commit into
Open
Conversation
odow
commented
Apr 2, 2025
| print_level = options["print_level"] | ||
| end | ||
| Ipopt.OpenIpoptOutputFile(prob, filename, print_level) | ||
| addOption(prob, "output_file", get(options, "output_file", "ipopt.out")) |
Author
There was a problem hiding this comment.
The options say https://coin-or.github.io/Ipopt/OPTIONS.html#OPT_output_file
But this is not needed for me locally:
julia> isfile("ipopt.out")
false
julia> using SNOW # http://flow.byu.edu/SNOW.jl/index.html
julia> function runOptimization(objFun, objFunGrad, conFun, conFunGrad, ng, x0, lx, ux, lc, uc, gradType)
function objCon!(g, x)
f = objFun(x)
g .= conFun(x)
return f
end
function objConGrad!(g, df, dg, x)
f = objFun(x)
df .= objFunGrad(x)
g .= conFun(x)
dg .= conFunGrad(x)
return f
end
ip_options = Dict(
"max_iter" => 1000,
"tol" => 1e-6,
"output_file" => "ipopt.out")
if gradType == "automatic"
options = Options(solver = IPOPT(ip_options),
derivatives=ForwardAD()) # choosing IPOPT solver
xopt, fopt, info = minimize(objCon!, x0, ng, lx, ux, lc, uc, options)
elseif gradType == "analytic"
options = Options(solver = IPOPT(ip_options),
derivatives=UserDeriv()) # choosing IPOPT solver
xopt, fopt, info = minimize(objConGrad!, x0, ng, lx, ux, lc, uc, options)
end
println("xstar = ", round.(xopt, digits=3))
println("fstar = ", round(fopt, digits=3))
println("info = ", info)
return xopt, fopt, info
end
runOptimization (generic function with 1 method)
julia> f(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2 # Objective function (Rosenbrock)
f (generic function with 1 method)
julia> ∇f(x) = [-2*(1.0 - x[1]) - 400*(x[2] - x[1]^2)*x[1], 200.0 * (x[2] - x[1]^2)]
∇f (generic function with 1 method)
julia> g1(x) = x[1]^2 + x[2]^2 - 1 # 1st constraint
g1 (generic function with 1 method)
julia> ∇g1(x) = [2*x[1], 2*x[2]]
∇g1 (generic function with 1 method)
julia> g2(x) = x[1] + 3*x[2] - 5 # 2nd constraint
g2 (generic function with 1 method)
julia> ∇g2(x) = [1.0, 3.0]
∇g2 (generic function with 1 method)
julia> g(x) = [g1(x); g2(x)]
g (generic function with 1 method)
julia> ∇g(x) = [∇g1(x)'; ∇g2(x)']
∇g (generic function with 1 method)
julia> ng = 2 # Number of constraints
2
julia> x0 = [1.3, 0.5] # Starting point
2-element Vector{Float64}:
1.3
0.5
julia> lx = [-2.0, -1.0] # Lower bound for design variables
2-element Vector{Float64}:
-2.0
-1.0
julia> ux = [+2.0, +3.0] # Upper bound for design variables
2-element Vector{Float64}:
2.0
3.0
julia> lc = [-Inf; -Inf] # Lower bound for nonlinear constraints
2-element Vector{Float64}:
-Inf
-Inf
julia> uc = [0.0; 0.0] # Upper bound for nonlinear constraints
2-element Vector{Float64}:
0.0
0.0
julia> gradType = "analytic" # options: automatic, analytic
"analytic"
julia> xopt, fopt, info = runOptimization(f, ∇f, g, ∇g, ng, x0, lx, ux, lc, uc, gradType)
This is Ipopt version 3.14.17, running with linear solver MUMPS 5.7.3.
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 4
Number of nonzeros in Lagrangian Hessian.............: 0
Total number of variables............................: 2
variables with only lower bounds: 0
variables with lower and upper bounds: 2
variables with only upper bounds: 0
Total number of equality constraints.................: 0
Total number of inequality constraints...............: 2
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 2
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.4170000e+02 9.40e-01 3.97e+01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.0650547e+01 1.70e+00 8.01e+01 -5.8 3.56e+01 - 2.50e-02 6.13e-02f 1
2 2.0242224e+01 1.70e+00 7.91e+01 -5.9 1.72e+00 - 1.66e-01 1.93e-02h 1
3 8.1098765e+01 5.11e-01 7.01e+01 -1.5 2.54e+00 - 6.47e-01 1.00e+00h 1
4 7.0226247e-01 0.00e+00 4.74e+00 -0.9 3.82e-01 - 1.00e+00 1.00e+00f 1
5 1.0811860e-01 0.00e+00 1.48e+00 -2.4 1.68e-01 - 1.00e+00 1.00e+00h 1
6 6.1736979e-02 0.00e+00 2.26e-02 -4.1 3.71e-02 - 1.00e+00 1.00e+00h 1
7 6.0347069e-02 0.00e+00 4.93e-02 -5.3 1.62e-02 - 1.00e+00 1.00e+00h 1
8 4.5768251e-02 2.58e-03 1.29e-01 -6.5 3.20e-01 - 1.00e+00 5.80e-01h 1
9 4.5834458e-02 0.00e+00 6.34e-02 -4.7 7.56e-03 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 4.5676766e-02 0.00e+00 2.12e-05 -6.7 1.35e-03 - 1.00e+00 1.00e+00h 1
11 4.5674807e-02 1.19e-10 9.53e-08 -11.0 2.58e-05 - 1.00e+00 9.99e-01h 1
Number of Iterations....: 11
(scaled) (unscaled)
Objective...............: 7.3740406022125646e-03 4.5674807490104635e-02
Dual infeasibility......: 9.5306477994326699e-08 5.9032832469685975e-07
Constraint violation....: 1.1878070222849125e-10 1.1878070222849125e-10
Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 1.2430637868727329e-11 7.6995370958897100e-11
Overall NLP error.......: 9.5306477994326699e-08 5.9032832469685975e-07
Number of objective function evaluations = 12
Number of objective gradient evaluations = 12
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 12
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 12
Number of Lagrangian Hessian evaluations = 0
Total seconds in IPOPT = 0.199
EXIT: Optimal Solution Found.
xstar = [0.786, 0.618]
fstar = 0.046
info = Solve_Succeeded
([0.7864151557284765, 0.6176983187279504], 0.045674807490104635, :Solve_Succeeded)
shell> cat ipopt.out
This is Ipopt version 3.14.17, running with linear solver MUMPS 5.7.3.
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 4
Number of nonzeros in Lagrangian Hessian.............: 0
Total number of variables............................: 2
variables with only lower bounds: 0
variables with lower and upper bounds: 2
variables with only upper bounds: 0
Total number of equality constraints.................: 0
Total number of inequality constraints...............: 2
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 2
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.4170000e+02 9.40e-01 3.97e+01 0.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.0650547e+01 1.70e+00 8.01e+01 -5.8 3.56e+01 - 2.50e-02 6.13e-02f 1
2 2.0242224e+01 1.70e+00 7.91e+01 -5.9 1.72e+00 - 1.66e-01 1.93e-02h 1
3 8.1098765e+01 5.11e-01 7.01e+01 -1.5 2.54e+00 - 6.47e-01 1.00e+00h 1
4 7.0226247e-01 0.00e+00 4.74e+00 -0.9 3.82e-01 - 1.00e+00 1.00e+00f 1
5 1.0811860e-01 0.00e+00 1.48e+00 -2.4 1.68e-01 - 1.00e+00 1.00e+00h 1
6 6.1736979e-02 0.00e+00 2.26e-02 -4.1 3.71e-02 - 1.00e+00 1.00e+00h 1
7 6.0347069e-02 0.00e+00 4.93e-02 -5.3 1.62e-02 - 1.00e+00 1.00e+00h 1
8 4.5768251e-02 2.58e-03 1.29e-01 -6.5 3.20e-01 - 1.00e+00 5.80e-01h 1
9 4.5834458e-02 0.00e+00 6.34e-02 -4.7 7.56e-03 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 4.5676766e-02 0.00e+00 2.12e-05 -6.7 1.35e-03 - 1.00e+00 1.00e+00h 1
11 4.5674807e-02 1.19e-10 9.53e-08 -11.0 2.58e-05 - 1.00e+00 9.99e-01h 1
Number of Iterations....: 11
(scaled) (unscaled)
Objective...............: 7.3740406022125646e-03 4.5674807490104635e-02
Dual infeasibility......: 9.5306477994326699e-08 5.9032832469685975e-07
Constraint violation....: 1.1878070222849125e-10 1.1878070222849125e-10
Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 1.2430637868727329e-11 7.6995370958897100e-11
Overall NLP error.......: 9.5306477994326699e-08 5.9032832469685975e-07
Number of objective function evaluations = 12
Number of objective gradient evaluations = 12
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 12
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 12
Number of Lagrangian Hessian evaluations = 0
Total seconds in IPOPT = 0.199
EXIT: Optimal Solution Found.
Author
There was a problem hiding this comment.
Maybe hold off until we resolve the discussion in https://discourse.julialang.org/t/ipopt-suppress-output-file-generation/127595/4
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
The file opened by OpenIpoptOutputFile is not released until FreeIpoptProblem is called. This is a problem with Julia's garbage collector, which is not guaranteed to run immediately. Thus, solving a model twice with the same log file can cause an error.
This was raised by https://discourse.julialang.org/t/ipopt-suppress-output-file-generation/127595
and also by jump-dev/Ipopt.jl#470
(I have no idea who two people independently opened the same issue on the same day!)