[SciPy-user] [OpenOpt] problem with ralg (latest SVN)

Nils Wagner nwagner@iam.uni-stuttgart...
Fri Sep 5 14:53:05 CDT 2008


On Fri, 05 Sep 2008 22:40:12 +0300
  dmitrey <dmitrey.kroshko@scipy.org> wrote:
> Hi Nils,
> after some my modifications the file nlp_1.py become 
>hard to be solved 
> by any solver (I mean connected to OO) - you can try 
>solving it by 
> algencan or ipopt and see results (output).
> 
> So I have committed some changes to nlp_1.py. As for 
>other tests (like 
> nlp_bench_1, nlp_3) they work ok (nlp_2 for ralg 
>requires p.maxIter = 2000).
> 
> Regards, D.
> 
> Nils Wagner wrote:
>> On Fri, 05 Sep 2008 20:28:50 +0300
>>   dmitrey <dmitrey.kroshko@scipy.org> wrote:
>>   
>>> Hi Emanuele,
>>> as it is mentioned in openopt install webpage and 
>>> README.txt  numpy v 
>>>     
>>>> = 1.1.0 is recommended. Some other oo users informed of 
>>>>       
>>> bugs due to 
>>> older versions.
>>>
>>> Could you inform what will be outputed if you set 
>>> p.debug = 1? (either 
>>> directly or via p = NLP(..., debug=1,...))
>>>
>>> If the problem with numpy versions is critical for users 
>>> of your soft, 
>>> you'd better to put more recent numpy into Debian soft 
>>> channel.
>>>
>>> Regards, D.
>>>
>>> Emanuele Olivetti wrote:
>>>     
>>>> Same problem with numpy 1.0.4 + scipy 0.6.0
>>>> (shipped with ubuntu 8.04 hardy heron).
>>>>
>>>> E.
>>>>
>>>> Emanuele Olivetti wrote:
>>>>   
>>>>       
>>>>> Dear all and Dmitrey,
>>>>>
>>>>> I've just updated to latest openopt (SVN). When using 
>>>>> numpy 1.0.3
>>>>> and scipy 0.5.2 (standard in Ubuntu 7.10 gutsy gibbon) 
>>>>> openopt says
>>>>> that "ralg" (NLP) algorithm is missing! With more recent 
>>>>> numpy
>>>>> and scipy it seems to work reliably. But what happened 
>>>>> with respect
>>>>> to older numpy+scipy? In that case even running 
>>>>> examples/nlp_1.py
>>>>> returns:
>>>>> ----
>>>>> $ python nlp_1.py
>>>>> OpenOpt checks user-supplied gradient df (shape: (150,) 
>>>>> )
>>>>> according to:
>>>>>     prob.diffInt = [  1.00000000e-07]
>>>>>     |1 - info_user/info_numerical| <= prob.maxViolation 
>>>>> = 0.01
>>>>> derivatives are equal
>>>>> ========================
>>>>> OpenOpt checks user-supplied gradient dc (shape: (2, 
>>>>> 150) )
>>>>> according to:
>>>>>     prob.diffInt = [  1.00000000e-07]
>>>>>     |1 - info_user/info_numerical| <= prob.maxViolation 
>>>>> = 0.01
>>>>> derivatives are equal
>>>>> ========================
>>>>> OpenOpt checks user-supplied gradient dh (shape: (2, 
>>>>> 150) )
>>>>> according to:
>>>>>     prob.diffInt = [  1.00000000e-07]
>>>>>     |1 - info_user/info_numerical| <= prob.maxViolation 
>>>>> = 0.01
>>>>> derivatives are equal
>>>>> ========================
>>>>> OO Error:incorrect solver is called, maybe the solver 
>>>>> "ralg" is not
>>>>> installed. Maybe setting p.debug=1 could specify the 
>>>>> matter more precisely
>>>>> Traceback (most recent call last):
>>>>>   File "nlp_1.py", line 110, in <module>
>>>>>     r = p.solve('ralg')
>>>>>   File
>>>>> "/usr/lib/python2.5/site-packages/scikits/openopt/Kernel/BaseProblem.py",
>>>>> line 185, in solve
>>>>>     return runProbSolver(self, solvers, *args, **kwargs)
>>>>>   File
>>>>> "/usr/lib/python2.5/site-packages/scikits/openopt/Kernel/runProbSolver.py",
>>>>> line 48, in runProbSolver
>>>>>     p.err('incorrect solver is called, maybe the solver 
>>>>> "' + solver_str
>>>>> +'" is not installed. Maybe setting p.debug=1 could 
>>>>> specify the matter
>>>>> more precisely')
>>>>>   File
>>>>> "/usr/lib/python2.5/site-packages/scikits/openopt/Kernel/oologfcn.py",
>>>>> line 16, in ooerr
>>>>>     raise OpenOptException(msg)
>>>>> scikits.openopt.Kernel.oologfcn.OpenOptException: 
>>>>> incorrect solver is
>>>>> called, maybe the solver "ralg" is not installed. Maybe 
>>>>> setting
>>>>> p.debug=1 could specify the matter more precisely
>>>>> ----
>>>>>
>>>>> This did not happen before so I guess it is due to a 
>>>>> recent
>>>>> commit. It is possible to solve the problem?
>>>>>
>>>>> Kind Regards,
>>>>>
>>>>> Emanuele
>>>>>
>>>>> _______________________________________________
>>>>> SciPy-user mailing list
>>>>> SciPy-user@scipy.org
>>>>> http://projects.scipy.org/mailman/listinfo/scipy-user
>>>>>
>>>>>   
>>>>>     
>>>>>         
>>>> _______________________________________________
>>>> SciPy-user mailing list
>>>> SciPy-user@scipy.org
>>>> http://projects.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>
>>>>
>>>>   
>>>>       
>>> _______________________________________________
>>> SciPy-user mailing list
>>> SciPy-user@scipy.org
>>> http://projects.scipy.org/mailman/listinfo/scipy-user
>>>     
>>
>>   
>> Dmitrey,
>>
>> I am using
>>   
>>>>> numpy.__version__
>>>>>         
>> '1.3.0.dev5790'
>>
>> Cheers,
>>           Nils
>>
>> Here comes the output of nlp_1.py:
>>
>> OpenOpt checks user-supplied gradient df (shape: (150,) 
>>)
>> according to:
>>      prob.diffInt = [  1.00000000e-07]
>>      |1 - info_user/info_numerical| <= prob.maxViolation 
>>= 
>> 0.01
>> derivatives are equal
>> ========================
>> OpenOpt checks user-supplied gradient dc (shape: (2, 
>>150) 
>> )
>> according to:
>>      prob.diffInt = [  1.00000000e-07]
>>      |1 - info_user/info_numerical| <= prob.maxViolation 
>>= 
>> 0.01
>> derivatives are equal
>> ========================
>> OpenOpt checks user-supplied gradient dh (shape: (2, 
>>150) 
>> )
>> according to:
>>      prob.diffInt = [  1.00000000e-07]
>>      |1 - info_user/info_numerical| <= prob.maxViolation 
>>= 
>> 0.01
>> derivatives are equal
>> ========================
>> -----------------------------------------------------
>> solver: ralg   problem: unnamed   goal: minimum
>>   iter    objFunVal    log10(maxResidual)
>>      0  8.596e+03               3.91
>> OpenOpt debug msg:  hs: 4.0
>> OpenOpt debug msg:  ls: 2
>>     50  2.800e+03               0.79
>>    100  1.754e+03               0.52
>>    150  9.075e+02               0.31
>>    200  4.455e+02              -0.03
>>    250  3.682e+02              -0.48
>>    300  3.465e+02              -1.15
>>    350  3.409e+02              -1.81
>>    400  1.911e+02              -3.14
>>    450  1.373e+02              -3.07
>> OO info:  debug msg: matrix B restoration in ralg solver
>>    500  1.065e+03               1.20
>>    550  2.224e+03               1.21
>>    600  1.822e+03               0.43
>>    650  2.178e+03               0.45
>>    700  2.576e+03               0.48
>>    750  2.840e+03               0.53
>>    800  3.068e+03               0.59
>>    850  7.958e+03               1.37
>>    900  2.174e+04               1.54
>>    950  3.341e+04               1.37
>>   1000  7.463e+04               2.17
>>   1050  3.692e+05               2.50
>>   1100  1.940e+05               2.16
>>   1150  1.482e+05               1.77
>>   1200  1.719e+05               1.86
>>   1250  2.963e+05               2.52
>>   1300  1.603e+05               2.27
>>   1350  2.299e+05               2.56
>>   1400  3.243e+05               2.63
>>   1450  2.663e+05               2.51
>>   1500  3.064e+05               2.55
>>   1550  4.297e+05               2.74
>>   1600  1.629e+05               2.80
>>   1650  2.379e+05               2.33
>>   1700  2.086e+05               2.28
>>   1750  1.214e+05               2.22
>>   1800  4.913e+04               1.58
>>   1850  3.862e+04               1.65
>>   1900  1.610e+05               2.53
>>   1950  3.576e+04               1.44
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   2000  7.286e+05               2.42
>>   2050  5.268e+05               2.50
>>   2100  1.403e+05               2.01
>>   2150  1.029e+05               1.96
>>   2200  9.997e+04               2.15
>>   2250  7.424e+05               2.92
>>   2300  5.514e+04               1.55
>>   2350  2.518e+05               2.66
>>   2400  5.051e+04               1.78
>>   2450  5.006e+04               2.05
>>   2500  4.279e+04               1.44
>>   2550  4.509e+04               1.62
>>   2600  1.331e+05               2.45
>>   2650  4.061e+04               1.41
>>   2700  5.198e+04               1.90
>>   2750  3.489e+09               4.77
>>   2800  6.938e+04               2.22
>>   2850  2.474e+10               5.20
>>   2900  4.259e+07               3.81
>>   2950  1.044e+05               2.40
>>   3000  6.411e+10               5.40
>>   3050  6.232e+07               3.89
>>   3100  1.830e+06               3.13
>>   3150  4.635e+04               1.45
>>   3200  1.770e+09               4.62
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   3250  1.764e+11               5.57
>>   3300  3.792e+09               4.01
>>   3350  1.554e+10               5.05
>>   3400  7.294e+09               4.81
>>   3450  7.227e+09               4.81
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   3500  1.415e+11               5.56
>>   3550  1.795e+10               6.16
>>   3600  5.205e+09               4.40
>>   3650  1.641e+10               5.04
>>   3700  1.408e+10               5.01
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   3750  1.277e+10               4.96
>>   3800  5.576e+09               3.89
>>   3850  5.008e+09               3.97
>>   3900  4.475e+09               4.04
>>   3950  3.993e+09               4.11
>>   4000  3.558e+09               4.17
>>   4050  3.237e+09               4.24
>>   4100  2.844e+09               4.24
>>   4150  1.077e+10               4.83
>>   4200  9.891e+09               4.82
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   4250  4.720e+09               4.12
>>   4300  3.411e+09               4.02
>>   4350  1.768e+09               6.43
>>   4400  1.851e+09               4.31
>>   4450  1.448e+09               3.99
>>   4500  1.248e+09               3.84
>>   4550  1.099e+09               3.80
>>   4600  6.053e+09               4.85
>>   4650  8.905e+08               3.86
>>   4700  1.446e+09               4.43
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   4750  6.292e+09               4.14
>>   4800  2.558e+09               3.96
>>   4850  2.898e+09               4.53
>>   4900  1.581e+09               4.21
>>   4950  1.272e+09               4.28
>>   5000  5.860e+09               6.34
>>   5050  4.163e+09               4.56
>>   5100  3.478e+09               4.22
>>   5150  3.238e+09               4.31
>>   5200  2.862e+09               3.92
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   5250  3.259e+09               4.36
>>   5300  2.207e+09               3.91
>>   5350  1.760e+09               3.74
>>   5400  1.560e+09               3.93
>>   5450  1.925e+09               4.41
>>   5500  1.739e+09               4.41
>>   5550  1.640e+09               4.42
>>   5600  8.408e+10               4.93
>>   5650  9.792e+10               4.69
>>   5700  1.303e+11               4.75
>>   5750  2.450e+11               5.44
>>   5800  4.913e+11               5.33
>>   5850  2.536e+11               6.00
>>   5900  3.098e+11               5.70
>>   5950  8.987e+10               5.37
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   6000  1.037e+12               6.00
>>   6050  3.448e+11               8.99
>>   6100  8.307e+12               6.40
>>   6150  1.589e+12               5.87
>>   6200  1.213e+12               5.27
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   6250  1.224e+12               5.45
>>   6300  7.495e+11               5.00
>>   6350  3.998e+11              15.67
>>   6400  3.987e+11               5.00
>>   6450  3.127e+11               5.02
>>   6500  2.419e+11               5.27
>>   6550  3.691e+11               5.13
>>   6600  6.414e+11               5.74
>>   6650  1.329e+12               5.92
>>   6700  3.528e+11               5.18
>>   6750  2.981e+11               4.78
>>   6800  5.060e+11               5.51
>>   6850  4.760e+11               5.09
>>   6900  4.499e+11               5.10
>>   6950  1.069e+12               5.86
>>   7000  6.326e+11               5.26
>>   7050  5.217e+11               5.18
>>   7100  5.029e+11               5.16
>>   7150  8.043e+12               6.43
>>   7200  1.073e+13               6.51
>>   7250  2.658e+12               6.18
>>   7300  2.053e+11               4.81
>>   7350  1.040e+12               5.45
>>   7400  2.030e+12               6.08
>>   7450  2.131e+12               6.11
>>   7500  3.493e+11               5.17
>>   7550  2.420e+11               5.04
>>   7600  2.344e+12               6.17
>>   7650  3.515e+11               5.62
>>   7700  2.135e+11               5.35
>>   7750  1.411e+11               4.78
>>   7800  8.295e+12               6.46
>>   7850  7.406e+12               6.39
>>   7900  9.030e+12               6.45
>>   7950  1.677e+12               6.04
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   8000  3.579e+12               6.23
>>   8050  1.109e+12              10.92
>>   8100  5.111e+12               5.80
>>   8150  7.521e+12               6.08
>>   8200  7.199e+12               5.85
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   8250  7.812e+12               6.05
>>   8300  5.366e+12               8.57
>>   8350  5.689e+12               5.97
>>   8400  5.140e+12               5.97
>>   8450  3.909e+12               5.38
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   8500  5.130e+12               6.12
>>   8550  3.753e+12               6.36
>>   8600  2.963e+12               5.43
>>   8650  2.528e+12               5.44
>>   8700  2.134e+12               5.46
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   8750  1.760e+12               5.46
>>   8800  1.467e+12               5.27
>>   8850  2.764e+12              12.53
>>   8900  2.152e+12               5.63
>>   8950  2.532e+12               5.86
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   9000  1.884e+12               5.67
>>   9050  4.073e+12              12.35
>>   9100  1.709e+12               5.38
>>   9150  1.398e+12               5.57
>>   9200  1.248e+12               5.60
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   9250  1.044e+12               5.14
>>   9300  7.844e+11               5.21
>>   9350  6.360e+11               5.47
>>   9400  6.253e+11               5.67
>>   9450  3.557e+11               4.91
>>   9500  3.400e+11               5.29
>>   9550  3.160e+11               5.30
>>   9600  2.601e+11               4.94
>>   9650  2.199e+11               4.85
>>   9700  5.335e+12              13.48
>> OO info:  debug msg: matrix B restoration in ralg solver
>>   9750  5.933e+12               6.24
>>   9800  4.174e+12               8.76
>>   9850  3.803e+12               5.52
>>   9900  2.854e+12               5.50
>>   9950  2.014e+12               5.47
>> 10000  3.285e+12               6.13
>> 10001  3.285e+12               6.13
>> istop:  -7 (Max Iter has been reached)
>> Solver:   Time Elapsed = 56.05  CPU Time Elapsed = 31.82
>> Plotting: Time Elapsed = 62.35  CPU Time Elapsed = 32.57
>> NO FEASIBLE SOLUTION is obtained (max residual = 
>>1.4e+06, 
>> objFunc = 3.2852899e+12)
>> _______________________________________________
>> SciPy-user mailing list
>> SciPy-user@scipy.org
>> http://projects.scipy.org/mailman/listinfo/scipy-user
>>
>>
>>
>>   
> 
> _______________________________________________
> SciPy-user mailing list
> SciPy-user@scipy.org
> http://projects.scipy.org/mailman/listinfo/scipy-user
  
Now it works for me
------------------------------------------------------------------------
r1270 | dmitrey.kroshko | 2008-09-05 21:33:49 +0200 (Fri, 
05 Sep 2008) | 1 line

some changes in nlp_1.py


OpenOpt checks user-supplied gradient df (shape: (150,) )
according to:
     prob.diffInt = [  1.00000000e-07]
     |1 - info_user/info_numerical| <= prob.maxViolation = 
0.01
derivatives are equal
========================
OpenOpt checks user-supplied gradient dc (shape: (2, 150) 
)
according to:
     prob.diffInt = [  1.00000000e-07]
     |1 - info_user/info_numerical| <= prob.maxViolation = 
0.01
derivatives are equal
========================
OpenOpt checks user-supplied gradient dh (shape: (2, 150) 
)
according to:
     prob.diffInt = [  1.00000000e-07]
     |1 - info_user/info_numerical| <= prob.maxViolation = 
0.01
derivatives are equal
========================
-----------------------------------------------------
solver: ralg   problem: unnamed   goal: minimum
  iter    objFunVal    log10(maxResidual)
     0  8.596e+03               5.73
OpenOpt debug msg:  hs: 16.0
OpenOpt debug msg:  ls: 4
    50  5.237e+03               1.08
   100  7.347e+03               1.04
   150  2.248e+04               1.24
   200  7.588e+03               1.24
   250  3.281e+03               0.74
   300  2.780e+03               0.59
   350  2.328e+03               0.52
   400  1.748e+03               0.39
   450  1.433e+03               0.27
   500  9.347e+02               0.10
   550  5.696e+02              -0.17
   600  4.870e+02              -0.46
   650  3.879e+02              -0.84
   700  3.319e+02              -1.35
   750  1.433e+02              -1.42
   800  1.444e+02              -1.46
   850  1.380e+02              -3.10
   900  1.337e+02              -3.03
   950  1.294e+02              -3.10
OO info:  debug msg: matrix B restoration in ralg solver
  1000  1.282e+02              -3.10
  1050  1.281e+02              -3.10
  1100  1.281e+02              -2.91
  1135  1.281e+02              -3.10
/usr/local/lib64/python2.5/site-packages/matplotlib/axes.py:4827: 
DeprecationWarning: replace "faceted=False" with 
"edgecolors='none'"
   DeprecationWarning)   #2008/04/18
istop:  3 (|| X[k] - X[k-1] || < xtol)
Solver:   Time Elapsed = 7.71   CPU Time Elapsed = 5.33
Plotting: Time Elapsed = 13.29  CPU Time Elapsed = 7.63
objFunValue: 128.08949 (feasible, max constraint = 
 0.0008)

Cheers,
             Nils


More information about the SciPy-user mailing list