ipoptによる非線形最適化のエラー

Question

前提・実現したいこと

ipoptによる最適化ができません。

発生している問題・エラーメッセージ

Ipopt 3.12.12: 
==> Warning: Treating 0 binary and 1000 integer variables as continous.


******************************************************************************
This program contains Ipopt, a library for large-scale nonlinear optimization.
 Ipopt is released as open source code under the Eclipse Public License (EPL).
         For more information visit http://projects.coin-or.org/Ipopt
******************************************************************************

This is Ipopt version 3.12.12, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).

Number of nonzeros in equality constraint Jacobian...:     1000
Number of nonzeros in inequality constraint Jacobian.:      540
Number of nonzeros in Lagrangian Hessian.............:    30300

Error in an AMPL evaluation. Run with "halt_on_ampl_error yes" to see details.
Error evaluating objective gradient at user provided starting point.
  No scaling factor for objective function computed!
Total number of variables............................:     1300
                     variables with only lower bounds:        0
                variables with lower and upper bounds:     1300
                     variables with only upper bounds:        0
Total number of equality constraints.................:      100
Total number of inequality constraints...............:      270
        inequality constraints with only lower bounds:        0
   inequality constraints with lower and upper bounds:        0
        inequality constraints with only upper bounds:      270

iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls
   0 -4.4966040e+02 9.00e-01 5.01e+01  -1.0 0.00e+00    -  0.00e+00 0.00e+00   0
   1 -9.8620678e+02 8.17e-01 9.22e+03  -1.0 1.07e-01   4.0 9.83e-01 9.25e-02f  1
   2 -1.0260026e+03 8.10e-01 9.64e+05  -1.0 8.17e-02   8.0 8.25e-01 7.98e-03f  1
   3 -1.0609083e+03 8.10e-01 4.52e+21  -1.0 8.10e-02  12.1 3.87e-01 8.83e-05f  1
WARNING: Problem in step computation; switching to emergency mode.
   4r-1.0609083e+03 8.10e-01 1.00e+03  -0.1 0.00e+00  20.0 0.00e+00 0.00e+00R  1
   5r-3.2534985e+03 4.76e-01 9.99e+02  -0.1 3.78e+02    -  7.97e-05 2.12e-03f  1
   6 -3.2537319e+03 4.76e-01 8.46e+07  -1.0 8.46e-07  14.0 1.00e+00 1.00e+00h  1
   7 -3.2888275e+03 4.76e-01 8.87e+08  -1.0 4.76e-02  13.5 4.54e-01 2.91e-04f  1
ERROR: Solver (ipopt) returned non-zero return code (-11)
ERROR: See the solver log above for diagnostic information.
---------------------------------------------------------------------------
ApplicationError                          Traceback (most recent call last)
<ipython-input-49-e31fc9a31c36> in <module>
     30 
     31 opt = SolverFactory("ipopt")
---> 32 result = opt.solve(model, tee=True)
     33 model.display()
     34 solver.options['print_level'] = 12

~/opt/anaconda3/lib/python3.7/site-packages/pyomo/opt/base/solvers.py in solve(self, *args, **kwds)
    594                     logger.error("Solver log:\n" + str(_status.log))
    595                 raise ApplicationError(
--> 596                     "Solver (%s) did not exit normally" % self.name)
    597             solve_completion_time = time.time()
    598             if self._report_timing:

ApplicationError: Solver (ipopt) did not exit normally

該当のソースコード

from pyomo.environ import *

I=100
J=30
T=10

model=ConcreteModel()
model.Candidate=range(I)
model.Item=range(J)
model.Class=range(T)

model.x=Var(model.Item,model.Class,bounds=(0.0,1.0))
model.y=Var(model.Candidate,model.Class,within=Binary)

#目的関数
model.obj=Objective( expr=sum( model.y[i,t]*u_ij[i,j]*log(model.x[j,t])+model.y[i,t]*(1-u_ij[i,j])*log(1-model.x[j,t]) for i in model.Candidate for j in model.Item for t in  model.Class ))
                              
                              
#制約条件1
model.mono=ConstraintList()
for j in model.Item:
    for t in model.Class:
        if t<=8:
            model.mono.add( model.x[j,t] <= model.x[j,t+1])

#制約条件2                              
model.single_y=ConstraintList()                              
for i in model.Candidate:
    model.single_y.add( sum( model.y[i,t] for t in model.Class ) == 1)
                              
opt = SolverFactory("ipopt")
result = opt.solve(model, tee=True)
model.display() 
solver.options['print_level'] = 12
solver.options['output_file'] = "my_ipopt_log.txt"