i’m having trouble debugging my NL model in Jump. Here is a MWE:
using JuMP, Ipopt
function j_test(a)
m = Model(Ipopt.Optimizer)
@variable(m,x >= 1.0)
@variable(m,y)
@NLexpression(m, q, x^a + log(x + a) )
@NLobjective(m, Min, y^2 + q)
optimize!(m)
(x = value(x), y = value(y))
end
If called with a positive paramter a, this works:
julia> j_test(10)
This is Ipopt version 3.13.2, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 2
Total number of variables............................: 2
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...............: 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 lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.5034260e+00 0.00e+00 1.00e+01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 3.4891590e+00 0.00e+00 3.81e-01 -1.0 5.20e-03 - 1.00e+00 2.50e-01f 3
2 3.4194021e+00 0.00e+00 1.63e-02 -1.7 6.59e-03 - 1.00e+00 1.00e+00f 1
3 3.3984007e+00 0.00e+00 1.54e-03 -3.8 2.06e-03 - 1.00e+00 1.00e+00f 1
4 3.3980456e+00 0.00e+00 4.44e-07 -3.8 3.52e-05 - 1.00e+00 1.00e+00f 1
5 3.3978970e+00 0.00e+00 7.81e-08 -5.7 1.47e-05 - 1.00e+00 1.00e+00f 1
6 3.3978952e+00 0.00e+00 1.22e-11 -8.6 1.85e-07 - 1.00e+00 1.00e+00f 1
Number of Iterations....: 6
(scaled) (unscaled)
Objective...............: 3.3978951743982369e+00 3.3978951743982369e+00
Dual infeasibility......: 1.2230216839270724e-11 1.2230216839270724e-11
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 2.5089529785247992e-09 2.5089529785247992e-09
Overall NLP error.......: 2.5089529785247992e-09 2.5089529785247992e-09
Number of objective function evaluations = 13
Number of objective gradient evaluations = 7
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 6
Total CPU secs in IPOPT (w/o function evaluations) = 0.004
Total CPU secs in NLP function evaluations = 0.000
EXIT: Optimal Solution Found.
(x = 1.0, y = 0.0)
with a = -1 there is a clear problem because at the lower bound of x we have log(0):
julia> j_test(-1.)
This is Ipopt version 3.13.2, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 2
Total number of variables............................: 2
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...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
...
ERROR: Problem in step computation, but emergency mode cannot be activated.
Number of Iterations....: 55
(scaled) (unscaled)
Objective...............: -3.1952610935758843e-07 -3.1952610935758841e+01
Dual infeasibility......: 2.0470907397013076e+06 2.0470907397013075e+14
Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00
Complementarity.........: 1.2544324387886203e-13 1.2544324387886202e-05
Overall NLP error.......: 2.0470907397013076e+06 2.0470907397013075e+14
Number of objective function evaluations = 81
Number of objective gradient evaluations = 56
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 56
Total CPU secs in IPOPT (w/o function evaluations) = 0.041
Total CPU secs in NLP function evaluations = 0.001
EXIT: Error in step computation (regularization becomes too large?)!
(x = 1.0000000000000049, y = 0.0)
and with a<1.0 one gets an immediate error
julia> j_test(-1.5)
This is Ipopt version 3.13.2, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 0
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 2
Total number of variables............................: 2
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...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
Number of Iterations....: 0
Number of objective function evaluations = 1
Number of objective gradient evaluations = 1
Number of equality constraint evaluations = 0
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 0
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 0
Total CPU secs in IPOPT (w/o function evaluations) = 0.000
Total CPU secs in NLP function evaluations = 0.000
EXIT: Invalid number in NLP function or derivative detected.
(x = 1.0099999899999998, y = 0.0)
I have a large model with many @NLexpressions. is there a way I can somehow check their value along the way after I call optimize?