I am currently using JuMP to solve an optimisation problem. Based on this;
Primal and dual warm-starts
it should be possible to initialize the primal and dual variables with the variables from the previous optimisation using the function set_optimal_start_values(model::Model). I added the function to my file, but I get this error message whenever I try to use it;
Exception has occurred:
MathOptInterface.UnsupportedAttribute{MathOptInterface.ConstraintPrimalStart}:
Attribute MathOptInterface.ConstraintPrimalStart() is not supported by the model.
I don't understand the error. It whould be nice if someone had experienced the same problem. Yes, I have added
"warm_start_init_point" => "yes"
to my JuMP model
The error says that Ipopt does not support setting a primal start for the constraints.
You should use
julia> using JuMP, Ipopt
julia> function set_optimal_start_values(model::Model)
# Store a mapping of the variable primal solution
variable_primal = Dict(x => value(x) for x in all_variables(model))
# In the following, we loop through every constraint and store a mapping
# from the constraint index to a tuple containing the primal and dual
# solutions.
constraint_solution = Dict()
nlp_dual_start = nonlinear_dual_start_value(model)
for (F, S) in list_of_constraint_types(model)
# We add a try-catch here because some constraint types might not
# support getting the primal or dual solution.
try
for ci in all_constraints(model, F, S)
constraint_solution[ci] = (value(ci), dual(ci))
end
catch
@info("Something went wrong getting $F-in-$S. Skipping")
end
end
# Now we can loop through our cached solutions and set the starting values.
for (x, primal_start) in variable_primal
set_start_value(x, primal_start)
end
for (ci, (primal_start, dual_start)) in constraint_solution
# set_start_value(ci, primal_start)
set_dual_start_value(ci, dual_start)
end
set_nonlinear_dual_start_value(model, nlp_dual_start)
return
end
set_optimal_start_values (generic function with 1 method)
julia> model = Model(Ipopt.Optimizer)
A JuMP Model
Feasibility problem with:
Variables: 0
Model mode: AUTOMATIC
CachingOptimizer state: EMPTY_OPTIMIZER
Solver name: Ipopt
julia> @variable(model, x >= 0)
x
julia> @NLconstraint(model, x^2 <= 1)
x ^ 2.0 - 1.0 ≤ 0
julia> @objective(model, Max, x)
x
julia> optimize!(model)
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.: 1
Number of nonzeros in Lagrangian Hessian.............: 1
Total number of variables............................: 1
variables with only lower bounds: 1
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 0
Total number of inequality constraints...............: 1
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 9.9999900e-03 0.00e+00 1.98e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.1773946e-01 0.00e+00 9.86e-01 -1.0 1.08e-01 - 5.58e-01 1.00e+00f 1
2 3.7890821e+00 1.34e+01 2.47e+00 -1.7 3.67e+00 - 6.52e-02 1.00e+00f 1
3 2.0163545e+00 3.07e+00 3.94e-01 -1.7 1.77e+00 - 1.00e+00 1.00e+00h 1
4 1.2438830e+00 5.47e-01 5.70e-02 -1.7 7.72e-01 - 1.00e+00 1.00e+00h 1
5 1.0080550e+00 1.62e-02 4.63e-02 -1.7 2.36e-01 - 1.00e+00 1.00e+00h 1
6 9.9794206e-01 0.00e+00 4.09e-04 -2.5 3.52e-02 - 1.00e+00 1.00e+00h 1
7 9.9984294e-01 0.00e+00 8.28e-06 -3.8 3.90e-03 - 1.00e+00 1.00e+00h 1
8 9.9999813e-01 0.00e+00 4.51e-08 -5.7 3.14e-04 - 1.00e+00 1.00e+00h 1
9 1.0000000e+00 0.00e+00 6.87e-12 -8.6 3.78e-06 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 9
(scaled) (unscaled)
Objective...............: -1.0000000024889628e+00 1.0000000024889628e+00
Dual infeasibility......: 6.8741856104894100e-12 6.8741856104894100e-12
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 2.5127966495526885e-09 2.5127966495526885e-09
Overall NLP error.......: 2.5127966495526885e-09 2.5127966495526885e-09
Number of objective function evaluations = 10
Number of objective gradient evaluations = 10
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 10
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 10
Number of Lagrangian Hessian evaluations = 9
Total seconds in IPOPT = 0.004
EXIT: Optimal Solution Found.
julia> set_optimal_start_values(model)
julia> optimize!(model)
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.: 1
Number of nonzeros in Lagrangian Hessian.............: 1
Total number of variables............................: 1
variables with only lower bounds: 1
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 0
Total number of inequality constraints...............: 1
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0000000e+00 0.00e+00 1.99e-09 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 9.8764269e-01 0.00e+00 1.17e-02 -1.7 1.47e-02 - 1.00e+00 1.00e+00h 1
2 9.9675597e-01 0.00e+00 4.71e-04 -2.5 1.82e-02 - 1.00e+00 1.00e+00h 1
3 9.9983859e-01 0.00e+00 1.56e-05 -3.8 6.23e-03 - 1.00e+00 1.00e+00h 1
4 9.9999813e-01 0.00e+00 4.55e-08 -5.7 3.29e-04 - 1.00e+00 1.00e+00h 1
5 1.0000000e+00 0.00e+00 6.88e-12 -8.6 3.78e-06 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 5
(scaled) (unscaled)
Objective...............: -1.0000000024889595e+00 1.0000000024889595e+00
Dual infeasibility......: 6.8763058686346261e-12 6.8763058686346261e-12
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 2.5128010673512823e-09 2.5128010673512823e-09
Overall NLP error.......: 2.5128010673512823e-09 2.5128010673512823e-09
Number of objective function evaluations = 6
Number of objective gradient evaluations = 6
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 6
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 6
Number of Lagrangian Hessian evaluations = 5
Total seconds in IPOPT = 0.005
EXIT: Optimal Solution Found.
See https://discourse.julialang.org/t/jump-model-warm-start-using-ipopt/92660 for an identical question on the JuMP community forum.