optimal with unscaled infeasibilities

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Iglesias Lopez, Araceli

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Feb 18, 2010, 6:21:41 AM2/18/10
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My model is optimal with unscaled infeasibilities in CPLEX 12.1.0 but optimal CPLEX 8.0.0 as you can see. What can I do?

 

 

 

 

 

 

 

IBM ILOG AMPL 12.1.0 (5724-Y45) AMPL Version 20090327 (x86_win32)

 

 

 

 

 

 

 

 

 

 

 

CPLEX 12.1.0: timing=1

 

 

 

 

 

 

prestats=1

 

 

 

 

 

 

 

 

 

mipdisplay=4

 

 

 

 

 

 

display=1

 

 

 

 

 

 

 

 

 

absmipgap=1e-4

 

 

 

 

 

 

mipgap=1e-4

 

 

 

 

 

 

 

 

 

time=1200

 

 

 

 

 

 

 

 

 

integrality=1e-6

 

 

 

 

 

 

startalgorithm=6

 

 

 

 

 

 

mipemphasis=1

 

 

 

 

 

 

mipalgorithm=1

 

 

 

 

 

 

mipcrossover=2

 

 

 

 

 

 

varselect=4

 

 

 

 

 

 

 

 

 

MIP Presolve eliminated 3935 rows and 5253 columns.

 

 

 

MIP Presolve modified 3106 coefficients.

 

 

 

 

 

Aggregator did 33425 substitutions.

 

 

 

 

 

Reduced MIP has 8940 rows, 15382 columns, and 96382 nonzeros.

 

 

 

Reduced MIP has 31 binaries, 0 generals, 0 SOSs, and 0 indicators.

 

 

 

Presolve time =    0.91 sec.

 

 

 

 

 

MIP emphasis: integer feasibility.

 

 

 

 

 

MIP search method: dynamic search.

 

 

 

 

 

Parallel mode: deterministic, using up to 2 threads.

 

 

 

 

Initializing dual steep norms . . .

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

        Nodes                                         Cuts/

 

 

 

 

   Node  Left     Objective  IInf  Best Integer     Best Node    ItCnt     Gap

 

 

 

 

 

 

 

 

 

 

      0     0   332251.0836    31                 332251.0836    30480         

 

 

*     0+    0                       331341.6153   332251.0836    30480    0.27%

 

 

      0     0   332241.2743    29   331341.6153      Cuts: 39    32325    0.27%

 

 

*     0+    0                       332161.7224   332241.2743    32325    0.02%

 

 

      0     0   332226.3792    31   332161.7224  Flowcuts: 20    34067    0.02%

 

 

*     0+    0                       332190.9978   332226.3792    34067    0.01%

 

 

      0     0   332217.1010    26   332190.9978  Flowcuts: 11    34932    0.01%

 

 

 

 

 

 

 

 

 

 

Flow cuts applied:  50

 

 

 

 

 

 

Gomory fractional cuts applied:  6

 

 

 

 

 

 

 

 

 

 

 

 

 

Root node processing (before b&c):

 

 

 

 

 

  Real time             =  112.02

 

 

 

 

 

Parallel b&c, 2 threads:

 

 

 

 

 

 

  Real time             =    0.00

 

 

 

 

 

  Sync time (average)   =    0.00

 

 

 

 

 

  Wait time (average)   =    0.00

 

 

 

 

 

                          -------

 

 

 

 

 

 

Total (root+branch&cut) =  112.02 sec.

 

 

 

 

 

Using devex.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Iteration log . . .

 

 

 

 

 

 

Iteration:     1    Scaled infeas =             0.000171

 

 

 

 

Iteration:   140    Objective     =        332190.995525

 

 

 

 

Iteration:   928    Objective     =        332190.995927

 

 

 

 

Iteration:  1138    Objective     =        332191.693179

 

 

 

 

Iteration:  1558    Objective     =        332192.271875

 

 

 

 

Iteration:  2048    Objective     =        332196.217383

 

 

 

 

Iteration:  2396    Objective     =        332205.917854

 

 

 

 

Iteration:  2657    Objective     =        332241.923135

 

 

 

 

Iteration:  2978    Objective     =        332250.878506

 

 

 

 

Elapsed time =   12.47 sec. (3000 iterations).

 

 

 

 

Removing shift (3).

 

 

 

 

 

 

Switched to devex.

 

 

 

 

 

 

Iteration:  3198    Infeasibility =            18.864545

 

 

 

 

Switched to devex.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Times (seconds):

 

 

 

 

 

 

Input =  0.374

 

 

 

 

 

 

Solve =  112.936

 

 

 

 

 

 

Output = 15.062

 

 

 

 

 

 

CPLEX solution status 5 with fixed integers:

 

 

 

 

 

optimal with unscaled infeasibilities

 

 

 

 

 

AMPL Version 20010215 Win32

 

CPLEX 8.0.0: timing=1

 

 

prestats=1

 

 

 

mipdisplay=4

 

 

display=1

 

 

 

absmipgap=1e-4

 

 

mipgap=1e-4

 

 

 

time=1200

 

 

 

integrality=1e-6

 

 

startalgorithm=6

 

 

mipemphasis=1

 

 

mipalgorithm=1

 

 

mipcrossover=2

 

 

varselect=4

 

 

 

MIP Presolve eliminated 3927 rows and 5307 columns.

 

MIP Presolve modified 4322 coefficients.

 

Aggregator did 33370 substitutions.

 

Reduced MIP has 9003 rows, 15383 columns, and 96302 nonzeros.

 

Presolve time =    3.14 sec.

 

MIP emphasis: integer feasibility

 

        Nodes                                         Cuts/

 

 

   Node  Left     Objective  IInf  Best Integer     Best Node    ItCnt     Gap

 

 

 

 

 

 

      0     0   332251.1095    31                 332251.1095    34129         

*     0+    0                   0   331212.7402   332251.1095    34129    0.31%

                332237.5432    25   331212.7402     Cuts:  54    36435    0.31%

*     0+    0                   0   332180.7057   332237.5432    36435    0.02%

                332214.7929    30   332180.7057     Cuts:  35    37803    0.01%

*     0+    0                   0   332190.9978   332214.7929    37803    0.01%

 

 

 

 

 

 

Flow cuts applied:  56

 

 

 

 

Gomory fractional cuts applied: 33

 

 

 

Using devex.

 

 

 

 

 

 

 

 

 

 

 

Iteration log . . .

 

 

 

 

Iteration:     1    Scaled infeas =             0.000181

 

 

Iteration:   584    Scaled infeas =             0.000034

 

 

Iteration:  1460    Objective     =        332190.997743

 

 

Elapsed time =   11.59 sec. (2000 iterations)

 

 

Iteration:  2288    Objective     =        332190.998142

 

 

Iteration:  3095    Objective     =        332190.998142

 

 

Iteration:  3636    Objective     =        332191.003646

 

 

Iteration:  4317    Objective     =        332191.004134

 

 

Iteration:  4829    Objective     =        332191.004149

 

 

Elapsed time =   25.50 sec. (5000 iterations)

 

 

Iteration:  5270    Objective     =        332196.412187

 

 

Iteration:  5612    Objective     =        332222.785130

 

 

Iteration:  5904    Objective     =        332249.655098

 

 

Elapsed time =   41.73 sec. (6000 iterations)

 

 

Iteration:  6197    Objective     =        332251.089589

 

 

Removing shift (5).

 

 

 

 

Iteration:  6214    Infeasibility =            18.849834

 

 

Iteration:  6552    Infeasibility =             0.966292

 

 

Iteration:  6792    Objective     =        332023.713057

 

 

Elapsed time =   56.66 sec. (7000 iterations)

 

 

Iteration:  7090    Objective     =        332168.454168

 

 

Iteration:  7383    Objective     =        332186.807481

 

 

Iteration:  7682    Objective     =        332190.097089

 

 

 

 

 

 

 

 

Times (seconds):

 

 

 

 

Input =  1.968

 

 

 

 

Solve =  404.373

 

 

 

 

Output = 72.14

 

 

 

 

 

 

 

 

 

 

CPLEX 8.0.0: optimal integer solution within mipgap or absmipgap; objective 332190.9978

37803 MIP simplex iterations

 

 

 

0 branch-and-bound nodes

 

 

 

 

Tried aggregator 2 times

 

 

 

 

 

Araceli

 



+Si ha recibido este correo electrónico por error, le informamos que puede contener información confidencial y que está prohibido su uso. Le rogamos lo comunique a su remitente y lo elimine.
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Robert Fourer

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Feb 20, 2010, 12:01:15 PM2/20/10
to am...@googlegroups.com, Iglesias Lopez, Araceli

Probably there were unscaled infeasibilities in the result from CPLEX 8 as well, but that condition was not reflected in the termination message.  CPLEX scales problems before solving them, to promote faster and more accurate solutions; then it unscales the results before reporting them back to AMPL.  The "unscaled infeasibilities" message indicates that CPLEX successfully solved the scaled problem to within its numerical tolerances for feasibility, but that after the variables were unscaled some of the constraints were no longer satisfied to within the tolerances.

 

Usually unscaled infeasibilities are the result of some coefficients in the problem being much larger in magnitudes than others.  If this is the case in your situation, then it may be worth thinking about how you could modify the formulation to reduce the variation in coefficient magnitudes.  In any case, if you are curious as to where the unscaled infeasibilities occur in your problem, you can use AMPL's display command to look for constraints that have a significant negative slack.

 

Bob Fourer

4...@ampl.com

 

 

From: am...@googlegroups.com [mailto:am...@googlegroups.com]

On Behalf Of Iglesias Lopez, Araceli [araceli....@cepsa.com]
Sent: Thursday, February 18, 2010 5:22 AM
To: am...@googlegroups.com
Subject: [AMPL 3316] optimal with unscaled infeasibilities

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