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

dmitrey dmitrey.kroshko@scipy....
Fri Sep 5 14:40:12 CDT 2008


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
>>>>
>>>> _______________________________________________
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>>>> SciPy-user@scipy.org
>>>> http://projects.scipy.org/mailman/listinfo/scipy-user
>>>>
>>>>   
>>>>     
>>>>         
>>> _______________________________________________
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>>>
>>>
>>>   
>>>       
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>>     
>
>   
> 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)
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