# Printing Jump NLexpression during solution

**URL:** <https://discourse.julialang.org/t/printing-jump-nlexpression-during-solution/49928>\
**Category:** Optimization (Mathematical)\
**Tags:** jump\
**Created:** [November 10, 2020, 5:54pm UTC](https://discourse.julialang.org/t/printing-jump-nlexpression-during-solution/49928 "2020-11-10T17:54:13Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![floswald](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/floswald/32/195_2.png) [@floswald](https://discourse.julialang.org/u/floswald)\
**Post date:** [November 10, 2020, 5:54pm UTC](https://discourse.julialang.org/t/printing-jump-nlexpression-during-solution/49928/1 "2020-11-10T17:54:13Z")

</div>

i’m having trouble debugging my NL model in Jump. Here is a MWE:

```julia
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
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
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
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 `@NLexpression`s. is there a way I can somehow check their value along the way after I call `optimize`?

---

<div class="post-metadata">

**Author:** ![odow](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/odow/32/28685_2.png) [@odow](https://discourse.julialang.org/u/odow)\
**Post date:** [November 11, 2020, 9:59pm UTC](https://discourse.julialang.org/t/printing-jump-nlexpression-during-solution/49928/2 "2020-11-11T21:59:38Z")

</div>

You can use `value(q)`, which will evaluate the expression at the current primal point (i.e., `value(x)`).

There is no easy way to show the values of the expressions during the solve. It requires a callback which isn’t accessible from JuMP at present.