Hello,
I am trying to use “Bonmin” or “Couenne” from AmplNLWriter in Julia. My code is attached below.
using JuMP, AmplNLWriter, Couenne_jll
#model = Model(() -> AmplNLWriter.Optimizer("bonmin"))
model = Model(() -> AmplNLWriter.Optimizer(Couenne_jll.amplexe))
v = [10, 20, 12, 23, 42]
w = [12, 45, 12, 22, 21]
@variable(model, x[1:5], Bin)
@objective(model, Max, v' * x)
@constraint(model, sum(w[i]*x[i]^2 for i in 1:5) <= 45)
optimize!(model)
println(termination_status(model))
println(objective_value(model))
println(value.(x))
I got the errors when running
“ERROR: ERROR: The provided optimizer_constructor is invalid. It must be callable with zero arguments. For example, “Ipopt.Optimizer” or “() → ECOS.Optimizer()”. It should not be an instantiated optimizer like “Ipopt.Optimizer()” or “ECOS.Optimizer()”. (Note the difference in parentheses!)”
I am new to Julia so I was wondering maybe there is something simple that I missed in my codes. Thank you for taking the time to read my post and for any help you can provide.
Thanks,
Emily
1 Like
The code you provided works perfectly on my end - do you have problems with specific versions of Julia/Packages?
Check my output below.
Ouput
A JuMP Model
Feasibility problem with:
Variables: 0
Model mode: AUTOMATIC
CachingOptimizer state: EMPTY_OPTIMIZER
Solver name: AmplNLWriter
5-element Vector{Int64}:
10
20
12
23
42
5-element Vector{Int64}:
12
45
12
22
21
5-element Vector{VariableRef}:
x[1]
x[2]
x[3]
x[4]
x[5]
10 x[1] + 20 x[2] + 12 x[3] + 23 x[4] + 42 x[5]
12 x[1]² + 45 x[2]² + 12 x[3]² + 22 x[4]² + 21 x[5]² ≤ 45
Couenne 0.5.8 -- an Open-Source solver for Mixed Integer Nonlinear Optimization
Mailing list: couenne@list.coin-or.org
Instructions: http://www.coin-or.org/Couenne
couenne:
ANALYSIS TEST: Couenne: new cutoff value 0.0000000000e+00 (0.075899 seconds)
NLP0012I
Num Status Obj It time Location
NLP0014I 1 OPT -77.755059 14 0.06618
Couenne: new cutoff value -6.4000000000e+01 (0.18193 seconds)
NLP0014I 2 OPT -64 0 0
Loaded instance "/tmp/jl_blQ19y/model.nl"
Constraints: 1
Variables: 5 (5 integer)
Auxiliaries: 7 (7 integer)
Coin0506I Presolve 17 (0) rows, 11 (-1) columns and 41 (-1) elements
Clp0006I 0 Obj -107 Primal inf 2.547618 (1)
Clp0006I 12 Obj -67
Clp0000I Optimal - objective value -67
Clp0032I Optimal objective -67 - 12 iterations time 0.002, Presolve 0.00
Clp0000I Optimal - objective value -67
NLP Heuristic: time limit reached.
Clp0000I Optimal - objective value -67
Optimality Based BT: Couenne: new cutoff value -6.5000000000e+01 (0.252534 seconds)
1 improved bounds
Probing: 0 improved bounds
NLP Heuristic: time limit reached.
Cbc0013I At root node, 0 cuts changed objective from -67 to -65 in 1 passes
Cbc0014I Cut generator 0 (Couenne convexifier cuts) - 0 row cuts average 0.0 elements, 5 column cuts (5 active)
Cbc0004I Integer solution of -65 found after 0 iterations and 0 nodes (0.03 seconds)
Cbc0001I Search completed - best objective -65, took 0 iterations and 0 nodes (0.03 seconds)
Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost
"Finished"
Linearization cuts added at root node: 17
Linearization cuts added in total: 17 (separation time: 0.001672s)
Total solve time: 0.039566s (0.039541s in branch-and-bound)
Lower bound: -65
Upper bound: -65 (gap: 0.00%)
Branch-and-bound nodes: 0
Performance of FBBT: 0.000828s, 4 runs. fix: 0 shrnk: 0 ubd: 0 2ubd: 0 infeas: 0
Performance of OBBT: 0.004393s, 1 runs. fix: 9 shrnk: 0 ubd: 0 2ubd: 0 infeas: 0
LOCALLY_SOLVED
65.0
[0.0, 0.0, 0.0, 1.0, 1.0]
2 Likes
Hello,
Thanks for the reply!
I got the problems solved. It worked on VS codes when running without debugging.
-Emily
2 Likes