我在python中使用pyomo跟随代码：
from pyomo.environ import *
mymodel=ConcreteModel()
var_x = Var([1,2], [0,1,2,3], domain=Reals)
var_y = Var([1,2], [0,1,2,3], domain=Reals)
var_z = Var([0,1,2,3], domain=Reals)
mymodel.q = var_x
mymodel.p = var_y
mymodel.u = var_z
express=(var_z[0] ** 2) + (var_y[1,0] * var_x[2,0]) + (var_y[2,0] * var_x[1,0])
cost_fn = Objective(expr=express)
mymodel.objective=cost_fn
sf = SolverFactory('couenne')
mymodel_results = sf.solve(mymodel)
print mymodel_results
上面的问题应该是相当微不足道的 - 显而易见的是获得最佳值只需设置所有变量= 0.
但是，couenne解算器会扼杀它，并在解决时一遍又一遍地打印相同的值。当我打印mymodel_results时，这就是我在Solver日志中看到的内容：
ERROR: "[base]/site-packages/pyomo/opt/base/solvers.py", 583, solve
Solver log:
Couenne 0.5.6 -- an Open-Source solver for Mixed Integer Nonlinear Optimization
Mailing list: couenne@list.coin-or.org
Instructions: http://www.coin-or.org/Couenne
Couenne: new cutoff value 0.0000000000e+00 (0.005058 seconds)
NLP0012I
Num Status Obj It time Location
NLP0014I 1 OPT 0 0 0.000188
Loaded instance "/var/folders/4f/z25gj3_d29b45_2nx87p8rvr0000gn/T/tmpsL5BN4.pyomo.nl"
Constraints: 0
Variables: 5 (0 integer)
Auxiliaries: 4 (0 integer)
Coin0506I Presolve 0 (-5) rows, 0 (-9) columns and 0 (-12) elements
Clp0000I Optimal - objective value -9.99e+12
Clp0032I Optimal objective -9.99e+12 - 0 iterations time 0.002, Presolve 0.00
Clp0000I Optimal - objective value -9.99e+12
NLP Heuristic: NLP0014I 2 OPT 0 0 0.000229
no solution.
Clp0000I Optimal - objective value -9.99e+12
Optimality Based BT: 0 improved bounds
Probing: 7 improved bounds
NLP Heuristic: no solution.
Cbc0013I At root node, 0 cuts changed objective from -9.99e+12 to -9.99e+12 in 1 passes
Cbc0014I Cut generator 0 (Couenne convexifier cuts) - 0 row cuts average 0.0 elements, 2 column cuts (2 active)
Cbc0010I After 0 nodes, 1 on tree, 1e+50 best solution, best possible -1.7976931e+308 (0.00 seconds)
Optimality Based BT: 1 improved bounds
Cbc0010I After 100 nodes, 50 on tree, 1e+50 best solution, best possible -9.99e+12 (4.52 seconds)
Cbc0010I After 200 nodes, 100 on tree, 1e+50 best solution, best possible -9.99e+12 (4.53 seconds)
....永远一样......
Cbc0010I After 16600 nodes, 105 on tree, 1e+50 best solution, best possible -9.99e+12 (8.51 seconds)
为什么容易出问题？
我知道有一个超时功能How to set Pyomo solver timeout?，但这个问题很简单，不需要它。此外，问题很容易通过ipopt解决，但couenne也应该处理它。