1. I have successfully used IPOPT with a wide range of Matlab versions on OS X.
Whether it works or not I’m sure depends on how your particular version of
IPOPT was built and which libraries it was linked against. Unfortunately,
sorting that out is beyond the scope of what I can help you with. I’d suggest
the Ipopt mailing list if you are having trouble getting it to work.
2. I think the answer here is … sometimes, but not necessarily always. It’s
probably worth giving it a try on your particular problem.
Ray
> On Feb 17, 2015, at 10:22 AM, Irina Boiarchuk <[email protected]>
> wrote:
>
> Dear all,
>
> my questions are about using IPOPT for optimization of large-scale systems.
>
> 1. Which MATLAB version do you have such that IPOPT works? (on their website
> it is said that there are some problems with R2014a of Matlab - i was
> wondering if it is still the case...)
>
> 2. Does IPOPT handle large-scale systems better than PDIPM or MIPS?
>
> I would appreciate any help regarding this issue!
>
> Kind regards,
> Irina Boiarchuk
>
>
>
>
>
> On 16 February 2015 at 22:49, Ray Zimmerman <[email protected]
> <mailto:[email protected]>> wrote:
> Absolutely.
>
> Ray
>
>> On Feb 16, 2015, at 12:11 PM, Bouchekara Houssem
>> <[email protected] <mailto:[email protected]>> wrote:
>>
>> Iam not using runopf but I have implemented my one OPF using MATPOWER
>> functions.
>> Another question, I guess that providing the Jacobian structure, Hessian and
>> Hessian structure will speed up the convergence?
>>
>>
>> On 2/16/2015 3:21 PM, Ray Zimmerman wrote:
>>> Again, it is not clear to me whether you are simply using MATPOWER’s OPF
>>> (i.e. runopf) or implementing your own. It would seem you’re implementing
>>> your own since the IPOPT output indicates that the Jacobian structure,
>>> Hessian and Hessian structure are not supplied. With MATPOWER’s OPF
>>> implementation that should not be the case. Here’s what I get …
>>>
>>> >> mpopt = mpoption('out.all', 0, 'verbose', 2, 'opf.ac.solver', 'IPOPT');
>>> >> runopf('case30', mpopt)
>>>
>>> MATPOWER Version 5.1-dev, 06-Feb-2015 -- AC Optimal Power Flow
>>> This is Ipopt version 3.10.3, running with linear solver mumps.
>>>
>>> Number of nonzeros in equality constraint Jacobian...: 454
>>> Number of nonzeros in inequality constraint Jacobian.: 324
>>> Number of nonzeros in Lagrangian Hessian.............: 254
>>>
>>> Total number of variables............................: 71
>>> variables with only lower bounds: 0
>>> variables with lower and upper bounds: 42
>>> variables with only upper bounds: 0
>>> Total number of equality constraints.................: 60
>>> Total number of inequality constraints...............: 82
>>> inequality constraints with only lower bounds: 0
>>> inequality constraints with lower and upper bounds: 0
>>> inequality constraints with only upper bounds: 82
>>>
>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>> alpha_pr ls
>>> 0 4.9036963e+02 1.21e+00 1.06e+01 0.0 0.00e+00 - 0.00e+00
>>> 0.00e+00 0
>>> 1 5.2670003e+02 4.79e-01 1.87e+02 0.1 1.28e+00 - 3.77e-01
>>> 5.58e-01h 1
>>> 2 5.7233174e+02 1.44e-02 8.54e+01 -0.4 6.07e-01 - 1.00e+00
>>> 1.00e+00h 1
>>> 3 5.7307970e+02 1.39e-02 5.98e+01 -1.0 9.22e-02 - 9.99e-01
>>> 2.91e-01h 1
>>> 4 5.7635279e+02 5.84e-03 1.90e+02 -1.3 1.49e-01 - 1.00e+00
>>> 1.00e+00h 1
>>> 5 5.7698733e+02 9.81e-04 6.93e+00 -2.0 6.20e-02 - 1.00e+00
>>> 1.00e+00h 1
>>> 6 5.7693657e+02 7.09e-05 5.84e+00 -2.9 1.30e-02 - 1.00e+00
>>> 1.00e+00h 1
>>> 7 5.7689674e+02 1.42e-05 6.89e-01 -3.8 7.65e-03 - 1.00e+00
>>> 1.00e+00h 1
>>> 8 5.7689223e+02 9.17e-06 5.46e-02 -5.3 6.06e-03 - 1.00e+00
>>> 1.00e+00h 1
>>> 9 5.7689227e+02 1.58e-06 1.08e-02 -6.8 2.65e-03 - 1.00e+00
>>> 1.00e+00h 1
>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>> alpha_pr ls
>>> 10 5.7689232e+02 2.06e-07 1.63e-03 -8.3 9.69e-04 - 1.00e+00
>>> 1.00e+00h 1
>>> 11 5.7689233e+02 1.32e-08 1.19e-04 -9.3 2.47e-04 - 1.00e+00
>>> 1.00e+00h 1
>>> 12 5.7689233e+02 1.13e-10 1.08e-06 -11.0 2.29e-05 - 1.00e+00
>>> 1.00e+00h 1
>>> 13 5.7689233e+02 1.36e-14 9.86e-11 -11.0 2.15e-07 - 1.00e+00
>>> 1.00e+00h 1
>>>
>>> Number of Iterations....: 13
>>>
>>> (scaled) (unscaled)
>>> Objective...............: 1.3985268672263120e+02 5.7689233273085370e+02
>>> Dual infeasibility......: 9.8586584428882276e-11 4.0666966076913940e-10
>>> Constraint violation....: 1.3558539391522494e-14 1.3558539391522494e-14
>>> Complementarity.........: 1.0529404232602505e-11 4.3433792459485328e-11
>>> Overall NLP error.......: 9.8586584428882276e-11 4.0666966076913940e-10
>>>
>>>
>>> Number of objective function evaluations = 14
>>> Number of objective gradient evaluations = 14
>>> Number of equality constraint evaluations = 14
>>> Number of inequality constraint evaluations = 14
>>> Number of equality constraint Jacobian evaluations = 14
>>> Number of inequality constraint Jacobian evaluations = 14
>>> Number of Lagrangian Hessian evaluations = 13
>>> Total CPU secs in IPOPT (w/o function evaluations) = 0.035
>>> Total CPU secs in NLP function evaluations = 0.046
>>>
>>> EXIT: Optimal Solution Found.
>>> >>
>>>
>>>
>>> Regarding the issue of the objective function decreasing with each
>>> iteration, that is not necessarily to be expected with primal-dual interior
>>> point solvers such as the one used by IPOPT. The objective function may
>>> need to increase in order to improve feasibility.
>>>
>>> Ray
>>>
>>>
>>>
>>>
>>>> On Feb 14, 2015, at 2:33 PM, Bouchekara Houssem
>>>> <[email protected] <mailto:[email protected]>> wrote:
>>>>
>>>> Dear all
>>>> I am trying to solve the OPF using the IPOPT solver (application on the
>>>> IEEE 30 bus test system).
>>>> However, I have some issues.
>>>>
>>>> The first one is that this solver takes many iterations and at the end, I
>>>> receive the message "Maximum Number of Iterations Exceeded.". if I
>>>> increase the number of iterations I obtain the same message on the time of
>>>> simulation !!!
>>>>
>>>> The second issue or question is that the objective function keep changing
>>>> but not always decreasing as you can see in the herewith results. I have
>>>> run the same problem using the fmincon function the objective function
>>>> does not increase at all it decreases all the time until convergence !!!
>>>>
>>>> Regards
>>>>
>>>>
>>>> Results
>>>> %-------------------------------------------------------------------------------------------------------------------
>>>> ------------------------------------------------------
>>>> Nonlinear Program (NLP) Optimization
>>>> min f(x)
>>>> s.t. lb <= x <= ub
>>>> cl <= c(x) <= cu
>>>> ------------------------------------------------------
>>>> Problem Properties:
>>>> # Decision Variables: 24
>>>> # Constraints: 233
>>>> # Bounds: 48
>>>> # Nonlinear Inequality: 185
>>>> ------------------------------------------------------
>>>> Solver Parameters:
>>>> Solver: IPOPT
>>>> Objective Gradient: @(x)mklJac(prob.fun,x,1) [numdiff]
>>>> Constraint Jacobian: @(x)mklJac(prob.nlcon,x,nnl) [numdiff]
>>>> Jacobian Structure: Not Supplied
>>>> Lagrangian Hessian: Not Supplied
>>>> Hessian Structure: Not Supplied
>>>> ------------------------------------------------------
>>>>
>>>> ******************************************************************************
>>>> 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
>>>> <http://projects.coin-or.org/Ipopt>
>>>> ******************************************************************************
>>>>
>>>> This is Ipopt version 3.11.7, running with linear solver ma57.
>>>>
>>>> Number of nonzeros in equality constraint Jacobian...: 0
>>>> Number of nonzeros in inequality constraint Jacobian.: 2712
>>>> Number of nonzeros in Lagrangian Hessian.............: 0
>>>>
>>>> Total number of variables............................: 24
>>>> variables with only lower bounds: 0
>>>> variables with lower and upper bounds: 24
>>>> variables with only upper bounds: 0
>>>> Total number of equality constraints.................: 0
>>>> Total number of inequality constraints...............: 113
>>>> inequality constraints with only lower bounds: 0
>>>> inequality constraints with lower and upper bounds: 72
>>>> inequality constraints with only upper bounds: 41
>>>>
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 0 8.2505796e+002 3.92e+001 2.39e+000 0.0 0.00e+000 - 0.00e+000
>>>> 0.00e+000 0
>>>> 1 8.2333913e+002 3.43e+001 2.88e+001 0.0 3.26e+000 - 4.40e-001
>>>> 7.91e-002f 2
>>>> 2 8.1521317e+002 0.00e+000 6.48e+001 -0.4 5.64e+000 - 8.84e-001
>>>> 1.00e+000f 1
>>>> 3 8.0493896e+002 0.00e+000 6.52e+001 0.0 3.96e+001 - 8.87e-001
>>>> 4.80e-001f 2
>>>> 4 8.0277302e+002 0.00e+000 2.39e+001 -0.1 3.95e+000 - 9.88e-001
>>>> 1.00e+000h 1
>>>> 5 8.0079336e+002 0.00e+000 3.27e+001 -0.8 5.65e+000 - 9.86e-001
>>>> 1.00e+000f 1
>>>> 6 7.9975232e+002 0.00e+000 1.50e+001 -1.3 1.64e+000 - 9.99e-001
>>>> 7.80e-001f 1
>>>> 7 7.9977509e+002 0.00e+000 1.70e+001 -1.1 1.52e+000 - 1.00e+000
>>>> 1.00e+000h 1
>>>> 8 7.9947341e+002 0.00e+000 2.73e+000 -1.7 7.49e-001 - 9.98e-001
>>>> 1.00e+000h 1
>>>> 9 7.9933411e+002 0.00e+000 1.06e+001 -2.2 2.68e+000 - 9.99e-001
>>>> 1.00e+000h 1
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 10 7.9931298e+002 0.00e+000 1.50e+001 -1.9 4.96e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 11 7.9945021e+002 0.00e+000 2.00e+001 -1.6 9.10e+000 - 1.00e+000
>>>> 4.53e-001f 2
>>>> 12 7.9940918e+002 0.00e+000 5.80e+000 -1.7 2.61e+000 - 7.69e-001
>>>> 1.00e+000h 1
>>>> 13 7.9935727e+002 0.00e+000 9.48e+000 -1.7 5.58e+000 - 8.33e-001
>>>> 2.62e-001h 2
>>>> 14 7.9970882e+002 0.00e+000 1.04e+001 -1.7 4.50e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 15 7.9936389e+002 0.00e+000 1.04e+001 -1.7 3.89e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 16 7.9932908e+002 0.00e+000 2.52e+000 -1.7 1.06e+001 - 5.30e-001
>>>> 8.14e-002h 3
>>>> 17 7.9940245e+002 0.00e+000 9.01e+000 -1.7 9.80e-001 - 1.00e+000
>>>> 1.00e+000h 1
>>>> 18 8.0110400e+002 0.00e+000 2.25e+001 -1.7 1.04e+001 - 5.27e-001
>>>> 1.00e+000H 1
>>>> 19 7.9946399e+002 0.00e+000 4.25e+000 -1.7 1.20e+001 - 1.00e+000
>>>> 1.00e+000F 1
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 20 7.9948550e+002 0.00e+000 1.00e+001 -1.3 1.24e+001 - 1.00e+000
>>>> 2.28e-001f 2
>>>> 21 7.9975197e+002 0.00e+000 1.65e+001 -1.5 3.80e+000 - 7.44e-001
>>>> 1.00e+000h 1
>>>> 22 8.0029390e+002 0.00e+000 4.95e+001 -1.5 4.78e+000 - 1.00e+000
>>>> 7.66e-001H 1
>>>> 23 7.9969885e+002 0.00e+000 3.75e+001 -1.5 7.79e+000 - 4.43e-001
>>>> 7.55e-001F 1
>>>> 24 8.0153952e+002 0.00e+000 5.42e+001 -1.5 6.09e+000 - 3.71e-001
>>>> 1.00e+000H 1
>>>> 25 8.0083849e+002 0.00e+000 5.53e+001 -1.5 9.48e+000 - 3.14e-001
>>>> 1.60e-001f 3
>>>> 26 8.0055898e+002 0.00e+000 5.68e+001 -1.5 7.24e+001 - 1.35e-001
>>>> 2.09e-002f 3
>>>> 27 7.9993130e+002 0.00e+000 2.14e+001 -1.5 1.56e+001 - 1.22e-001
>>>> 1.16e-001f 2
>>>> 28 7.9966609e+002 0.00e+000 2.34e+001 -1.5 1.13e+001 - 1.91e-001
>>>> 2.55e-001h 1
>>>> 29 7.9951187e+002 0.00e+000 4.68e+000 -1.5 1.61e+001 - 7.25e-002
>>>> 2.62e-001f 1
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 30 8.0267261e+002 0.00e+000 2.31e+001 -1.5 9.14e+000 - 6.12e-001
>>>> 9.49e-001H 1
>>>> 31 8.0226367e+002 0.00e+000 1.04e+001 -1.5 1.01e+001 - 3.30e-001
>>>> 8.99e-002f 3
>>>> 32 8.0192666e+002 0.00e+000 1.32e+001 -1.5 2.47e+001 - 8.06e-002
>>>> 2.93e-002f 4
>>>> 33 8.0132986e+002 0.00e+000 9.01e+000 -1.5 1.40e+001 - 5.76e-001
>>>> 1.32e-001f 1
>>>> 34 8.0105092e+002 0.00e+000 6.84e+000 -1.5 7.34e+000 - 8.30e-001
>>>> 9.60e-002f 3
>>>> 35 7.9977021e+002 0.00e+000 3.28e+001 -1.5 9.26e+000 - 3.27e-001
>>>> 8.37e-001F 1
>>>> 36 8.0033089e+002 0.00e+000 4.86e+001 -1.5 4.62e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 37 7.9996104e+002 0.00e+000 1.06e+001 -1.5 5.43e+000 - 1.96e-001
>>>> 1.75e-001f 2
>>>> 38 7.9951421e+002 0.00e+000 9.42e+000 -1.5 1.59e+001 - 2.53e-001
>>>> 1.96e-001f 2
>>>> 39 7.9967929e+002 0.00e+000 3.01e+001 -1.5 3.16e+000 - 9.19e-001
>>>> 7.79e-001H 1
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 40 7.9962530e+002 0.00e+000 2.19e+001 -1.5 6.91e+000 - 1.80e-001
>>>> 1.78e-001h 3
>>>> 41 7.9951740e+002 0.00e+000 1.26e+000 -1.5 1.88e+000 - 1.00e+000
>>>> 1.00e+000h 1
>>>> 42 8.0007055e+002 0.00e+000 9.26e+000 -1.5 3.65e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 43 7.9994265e+002 0.00e+000 7.74e+000 -1.5 7.16e+000 - 5.93e-001
>>>> 9.68e-002h 4
>>>> 44 7.9969538e+002 0.00e+000 6.51e+000 -1.5 7.45e+000 - 6.32e-001
>>>> 1.92e-001h 2
>>>> 45 7.9957525e+002 0.00e+000 4.13e+000 -1.5 1.30e+001 - 4.92e-001
>>>> 5.31e-002h 3
>>>> 46 7.9993363e+002 0.00e+000 2.71e+001 -1.5 4.17e+000 - 4.89e-001
>>>> 1.00e+000H 1
>>>> 47 7.9973977e+002 0.00e+000 1.60e+001 -1.5 5.74e+000 - 6.88e-001
>>>> 3.02e-001h 2
>>>> 48 7.9989765e+002 0.00e+000 2.33e+001 -1.5 4.88e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 49 7.9983511e+002 0.00e+000 2.31e+001 -1.5 1.74e+001 - 6.39e-001
>>>> 5.01e-002h 5
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 50 7.9970425e+002 0.00e+000 1.59e+001 -1.5 2.09e+001 - 2.33e-001
>>>> 8.10e-002h 3
>>>> 51 7.9955418e+002 0.00e+000 5.09e+000 -1.5 1.94e+001 - 2.51e-001
>>>> 6.63e-002f 2
>>>> 52 7.9950139e+002 0.00e+000 3.11e+000 -1.5 6.47e+000 - 4.13e-001
>>>> 8.04e-002h 4
>>>> 53 7.9944413e+002 0.00e+000 1.92e+000 -1.5 3.36e+000 - 8.24e-001
>>>> 2.06e-001h 3
>>>> 54 8.0126819e+002 0.00e+000 3.56e+001 -1.5 8.76e+000 - 6.44e-001
>>>> 1.00e+000H 1
>>>> 55 7.9945686e+002 0.00e+000 2.26e+000 -1.5 9.10e+000 - 1.00e+000
>>>> 1.00e+000F 1
>>>> 56 7.9950057e+002 0.00e+000 1.04e+001 -1.5 1.95e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 57 7.9942217e+002 0.00e+000 1.84e+001 -1.7 1.70e+000 - 1.00e+000
>>>> 1.00e+000h 1
>>>> 58 8.0023640e+002 0.00e+000 6.79e+001 -1.8 2.88e+000 - 2.83e-001
>>>> 1.00e+000H 1
>>>> 59 8.0004652e+002 0.00e+000 5.87e+001 -1.8 6.11e+000 - 5.91e-001
>>>> 9.88e-002h 4
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 60 7.9981714e+002 0.00e+000 5.01e+001 -1.8 2.04e+001 - 7.88e-002
>>>> 4.40e-002f 4
>>>> 61 7.9975653e+002 0.00e+000 4.73e+001 -1.8 9.31e+000 - 8.23e-001
>>>> 2.59e-002h 4
>>>> 62 7.9970454e+002 0.00e+000 4.19e+001 -1.8 3.96e+000 - 4.82e-001
>>>> 3.85e-002h 5
>>>> 63 7.9962939e+002 0.00e+000 3.88e+001 -1.8 4.97e+000 - 2.21e-001
>>>> 5.05e-002h 5
>>>> 64 7.9953788e+002 0.00e+000 3.19e+001 -1.8 1.07e+001 - 3.03e-001
>>>> 1.32e-001h 2
>>>> 65 7.9939133e+002 0.00e+000 1.33e+001 -1.8 1.27e+001 - 2.71e-001
>>>> 1.24e-001h 2
>>>> 66 7.9939690e+002 0.00e+000 4.01e+000 -1.8 7.99e+000 - 3.71e-001
>>>> 4.06e-001h 2
>>>> 67 7.9936697e+002 0.00e+000 4.73e+000 -1.8 4.44e+001 - 3.91e-001
>>>> 3.41e-002h 4
>>>> 68 7.9936784e+002 0.00e+000 1.89e+000 -1.8 1.39e+001 - 9.19e-001
>>>> 1.23e-001h 4
>>>> 69 7.9943394e+002 0.00e+000 1.13e+001 -1.8 7.41e+000 - 3.90e-001
>>>> 3.38e-001w 1
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 70 8.0333982e+002 3.88e-001 6.64e+001 -1.8 1.21e+001 - 4.03e-001
>>>> 9.33e-001w 1
>>>> 71 8.0004084e+002 2.01e+000 2.23e+001 -1.8 9.50e+000 - 1.75e-001
>>>> 8.22e-001w 1
>>>> 72 7.9932137e+002 0.00e+000 4.72e+000 -1.8 8.14e+001 - 3.90e-001
>>>> 1.69e-001h 1
>>>> 73 7.9933546e+002 0.00e+000 1.20e+001 -1.8 2.20e+000 - 1.00e+000
>>>> 4.15e-001h 2
>>>> 74 7.9930951e+002 0.00e+000 8.03e+000 -1.8 2.31e+001 - 2.28e-001
>>>> 5.44e-002h 4
>>>> 75 8.0016365e+002 0.00e+000 6.14e+001 -1.9 2.00e+000 - 9.59e-001
>>>> 1.00e+000H 1
>>>> 76 7.9955770e+002 0.00e+000 4.24e+001 -2.0 5.35e+000 - 7.40e-001
>>>> 4.29e-001f 1
>>>> 77 7.9939342e+002 0.00e+000 1.45e+001 -2.0 2.40e+001 - 1.52e-001
>>>> 9.57e-002h 2
>>>> 78 7.9925263e+002 0.00e+000 1.21e+001 -2.0 1.69e+001 - 1.18e-001
>>>> 1.51e-001h 2
>>>> 79 7.9941302e+002 0.00e+000 1.24e+001 -1.3 1.89e+001 - 9.77e-001
>>>> 1.77e-001h 2
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 80 8.0134560e+002 0.00e+000 3.00e+001 -1.4 4.81e+000 - 6.41e-001
>>>> 1.00e+000h 1
>>>> 81 7.9998500e+002 0.00e+000 1.73e+001 -1.4 7.31e+000 - 9.05e-001
>>>> 8.39e-001f 1
>>>> 82 7.9953189e+002 0.00e+000 4.34e+000 -1.4 2.20e+001 - 7.03e-001
>>>> 1.77e-001f 2
>>>> 83 7.9959732e+002 0.00e+000 1.07e+001 -1.4 3.78e+000 - 6.33e-001
>>>> 1.00e+000h 1
>>>> 84 8.0004713e+002 0.00e+000 3.44e+001 -1.4 4.05e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 85 7.9988950e+002 0.00e+000 3.31e+001 -1.4 3.13e+001 - 5.62e-001
>>>> 9.57e-002f 2
>>>> 86 7.9955465e+002 0.00e+000 1.76e+001 -1.4 1.50e+000 - 1.00e+000
>>>> 1.00e+000h 1
>>>> 87 7.9949662e+002 0.00e+000 7.54e+000 -1.4 3.70e+000 - 2.85e-001
>>>> 1.85e-001h 3
>>>> 88 7.9959288e+002 0.00e+000 1.71e+001 -1.4 1.39e+000 - 1.00e+000
>>>> 1.00e+000h 1
>>>> 89 8.0006272e+002 0.00e+000 1.37e+001 -1.4 5.26e+000 - 1.00e+000
>>>> 1.00e+000h 1
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 90 7.9980392e+002 0.00e+000 1.44e+001 -1.4 6.97e+000 - 4.93e-001
>>>> 2.01e-001h 2
>>>> 91 7.9963251e+002 0.00e+000 8.20e+000 -1.4 9.82e+000 - 5.22e-001
>>>> 1.65e-001h 3
>>>> 92 7.9959672e+002 0.00e+000 2.45e+000 -1.4 3.24e+001 - 7.30e-001
>>>> 1.55e-001h 2
>>>> 93 7.9951429e+002 0.00e+000 3.42e+000 -1.4 3.75e+000 - 8.01e-001
>>>> 1.00e+000h 1
>>>> 94 7.9972009e+002 0.00e+000 1.76e+001 -1.4 2.53e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> 95 7.9971154e+002 0.00e+000 2.36e+001 -1.4 2.37e+000 - 7.73e-001
>>>> 1.00e+000H 1
>>>> 96 7.9958228e+002 0.00e+000 1.80e+001 -1.4 1.65e+001 - 4.70e-001
>>>> 1.12e-001h 2
>>>> 97 7.9950114e+002 0.00e+000 7.95e+000 -1.4 3.83e+000 - 1.00e+000
>>>> 2.99e-001h 2
>>>> 98 7.9964810e+002 0.00e+000 1.43e+001 -1.4 2.33e+000 - 9.82e-001
>>>> 1.00e+000H 1
>>>> 99 7.9958083e+002 0.00e+000 9.04e+000 -1.4 2.42e+000 - 1.00e+000
>>>> 1.00e+000H 1
>>>> iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du
>>>> alpha_pr ls
>>>> 100 7.9957133e+002 0.00e+000 5.14e+000 -1.4 1.19e+001 - 4.74e-001
>>>> 1.06e-001h 3
>>>>
>>>> Number of Iterations....: 100
>>>>
>>>> (scaled) (unscaled)
>>>> Objective...............: 7.9957132849034247e+002
>>>> 7.9957132849034247e+002
>>>> Dual infeasibility......: 5.1351489054861581e+000
>>>> 5.1351489054861581e+000
>>>> Constraint violation....: 0.0000000000000000e+000
>>>> 0.0000000000000000e+000
>>>> Complementarity.........: 6.3178225777153776e-002
>>>> 6.3178225777153776e-002
>>>> Overall NLP error.......: 5.1351489054861581e+000
>>>> 5.1351489054861581e+000
>>>>
>>>>
>>>> Number of objective function evaluations = 331
>>>> Number of objective gradient evaluations = 101
>>>> Number of equality constraint evaluations = 0
>>>> Number of inequality constraint evaluations = 331
>>>> Number of equality constraint Jacobian evaluations = 0
>>>> Number of inequality constraint Jacobian evaluations = 101
>>>> Number of Lagrangian Hessian evaluations = 0
>>>> Total CPU secs in IPOPT (w/o function evaluations) = 2.533
>>>> Total CPU secs in NLP function evaluations = 125.944
>>>>
>>>> EXIT: Maximum Number of Iterations Exceeded.
>>>> %-------------------------------------------------------------------------------------------------------------------
>>>
>>
>>
>
>
