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We should probably error out when given a model that uses the old API. But I don't know how to detect this properly at the MOI level.
julia> using JuMP, ExaModels, MadNLP
julia> m = Model()
A JuMP Model
├ solver: none
├ objective_sense: FEASIBILITY_SENSE
├ num_variables: 0
├ num_constraints: 0
└ Names registered in the model: none
julia> @variable m x
x
julia> @objective m Min x^2 + x + 1
x² + x + 1
julia> madnlp(ExaModel(m))
This is MadNLP version v0.8.12, running with umfpack
Number of nonzeros in constraint Jacobian............: 0
Number of nonzeros in Lagrangian Hessian.............: 1
Total number of variables............................: 1
variables with only lower bounds: 0
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 0
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 inf_compl lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 1.0000000e+00 0.00e+00 1.00e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 7.5000000e-01 0.00e+00 0.00e+00 0.00e+00 -1.7 5.00e-01 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 1
(scaled) (unscaled)
Objective...............: 7.5000000000000000e-01 7.5000000000000000e-01
Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00
Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00
Number of objective function evaluations = 2
Number of objective gradient evaluations = 2
Number of constraint evaluations = 2
Number of constraint Jacobian evaluations = 2
Number of Lagrangian Hessian evaluations = 1
Total wall-clock secs in solver (w/o fun. eval./lin. alg.) = 14.406
Total wall-clock secs in linear solver = 0.027
Total wall-clock secs in NLP function evaluations = 0.000
Total wall-clock secs = 14.433
EXIT: Optimal Solution Found (tol = 1.0e-08).
"Execution stats: Optimal Solution Found (tol = 1.0e-08)."julia> mold = Model()
A JuMP Model
├ solver: none
├ objective_sense: FEASIBILITY_SENSE
├ num_variables: 0
├ num_constraints: 0
└ Names registered in the model: none
julia> @variable mold x
x
julia> @NLobjective mold Min x^2 + x + 1
julia> madnlp(ExaModel(mold))
This is MadNLP version v0.8.12, running with umfpack
Number of nonzeros in constraint Jacobian............: 0
Number of nonzeros in Lagrangian Hessian.............: 0
Total number of variables............................: 1
variables with only lower bounds: 0
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 0
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 inf_compl lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 0.0000000e+00 0.00e+00 0.00e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
Number of Iterations....: 0
(scaled) (unscaled)
Objective...............: 0.0000000000000000e+00 0.0000000000000000e+00
Dual infeasibility......: 0.0000000000000000e+00 0.0000000000000000e+00
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00
Overall NLP error.......: 0.0000000000000000e+00 0.0000000000000000e+00
Number of objective function evaluations = 1
Number of objective gradient evaluations = 1
Number of constraint evaluations = 1
Number of constraint Jacobian evaluations = 1
Number of Lagrangian Hessian evaluations = 1
Total wall-clock secs in solver (w/o fun. eval./lin. alg.) = 5.709
Total wall-clock secs in linear solver = 0.000
Total wall-clock secs in NLP function evaluations = 0.000
Total wall-clock secs = 5.709
EXIT: Optimal Solution Found (tol = 1.0e-08).
"Execution stats: Optimal Solution Found (tol = 1.0e-08)."Metadata
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