Error with Ipopt.jl and ipopt.out file #470
Description
Hi,
I have a simple test code, that used to work and now gives an error.
When I run the code the first time, Ipopt manages to solve the toy problem and print the output to a .txt file.
The second time I run the code, without killing the Julia REPL/terminal, the output file is still held by some process and the code cannot run because it can not access the output file.
The error:
ERROR: IPOPT: Couldn't open output file.
Stacktrace:
[1] error(s::String)
@ Base .\error.jl:35
[2] OpenIpoptOutputFile
@ C:\Users\nicolo\.julia\packages\Ipopt\Tn8LH\src\C_wrapper.jl:355 [inlined]
[3] optimize(solver::IPOPT{…}, cache::SNOW.DenseCache{…}, x0::Vector{…}, lx::Vector{…}, ux::Vector{…}, lg::Vector{…}, ug::Vector{…}, rows::Vector{…}, cols::Vector{…})
@ SNOW C:\Users\nicolo\.julia\packages\SNOW\E3ZCP\src\ipopt.jl:118
[4] minimize(func!::Function, x0::Vector{…}, ng::Int64, lx::Vector{…}, ux::Vector{…}, lg::Vector{…}, ug::Vector{…}, options::Options{…})
@ SNOW C:\Users\nicolo\.julia\packages\SNOW\E3ZCP\src\interface.jl:73
[5] runOptimization(objFun::typeof(f), objFunGrad::typeof(∇f), conFun::typeof(g), conFunGrad::typeof(∇g), ng::Int64, x0::Vector{…}, lx::Vector{…}, ux::Vector{…}, lc::Vector{…}, uc::Vector{…}, gradType::String)
@ Main c:\Users\nicolo\Dropbox\Work\Code\Julia\Tests\Test_SNOW_IPOPT\Use_SNOWjl.jl:33
[6] top-level scope
@ c:\Users\nicolo\Dropbox\Work\Code\Julia\Tests\Test_SNOW_IPOPT\Use_SNOWjl.jl:64
Some type information was truncated. Use `show(err)` to see complete types.'
Julia version: 1.11.4
Ipopt.jl v1.8.0
Code giving the issue:
using SNOW # http://flow.byu.edu/SNOW.jl/index.html
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
f(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2 # Objective function (Rosenbrock)
∇f(x) = [-2*(1.0 - x[1]) - 400*(x[2] - x[1]^2)*x[1], 200.0 * (x[2] - x[1]^2)]
g1(x) = x[1]^2 + x[2]^2 - 1 # 1st constraint
∇g1(x) = [2*x[1], 2*x[2]]
g2(x) = x[1] + 3*x[2] - 5 # 2nd constraint
∇g2(x) = [1.0, 3.0]
g(x) = [g1(x); g2(x)]
∇g(x) = [∇g1(x)'; ∇g2(x)']
ng = 2 # Number of constraints
x0 = [1.3, 0.5] # Starting point
lx = [-2.0, -1.0] # Lower bound for design variables
ux = [+2.0, +3.0] # Upper bound for design variables
lc = [-Inf; -Inf] # Lower bound for nonlinear constraints
uc = [0.0; 0.0] # Upper bound for nonlinear constraints
gradType = "analytic" # options: automatic, analytic
xopt, fopt, info = runOptimization(f, ∇f, g, ∇g, ng, x0, lx, ux, lc, uc, gradType)