Bug #1074
Optimizer does not converge for parameters close to truth
| Status: | Closed | Start date: | 01/09/2014 | |
|---|---|---|---|---|
| Priority: | Urgent | Due date: | ||
| Assigned To: |  Knödlseder Jürgen | % Done: | 100% | |
| Category: | - | |||
| Target version: | 1.0.0 | |||
| Duration: |
Description
Using the “Getting started example” of the ctools page, the unbinned ctlike fit did not properly converge for the following run:
$ ctlike Event list, counts map or observation definition file [selected_events.fits] Calibration database [$GAMMALIB/share/caldb/cta] Instrument response function [cta_dummy_irf] Source model [crab.xml] $GAMMALIB/share/models/crab.xml Source model output file [crab_results.xml]
The resulting log file is:
2014-01-09T21:49:39: Initial iteration: func=44391.3, Lambda=0.001 2014-01-09T21:49:39: Iteration 1: func=44391.3, Lambda=0.01, delta=0, max(grad)=9.59982 [6] (stalled) 2014-01-09T21:49:39: Iteration 2: func=44391.3, Lambda=0.1, delta=0, max(grad)=8.93495 [6] (stalled) 2014-01-09T21:49:39: Iteration 3: func=44391.3, Lambda=1, delta=0, max(grad)=3.15298 [6] (stalled) 2014-01-09T21:49:39: Iteration 4: func=44388.3, Lambda=0.1, delta=2.9745, max(grad)=0 [-1] 2014-01-09T21:49:39: Iteration 5: func=44388.3, Lambda=1, delta=0, max(grad)=8.9891 [6] (stalled) 2014-01-09T21:49:39: Iteration 6: func=44387.4, Lambda=0.1, delta=0.897666, max(grad)=2.69155 [7] 2014-01-09T21:49:39: Iteration 7: func=44387.4, Lambda=1, delta=0, max(grad)=11.4893 [6] (stalled) 2014-01-09T21:49:39: Iteration 8: func=44387.4, Lambda=10, delta=0, max(grad)=12.8144 [7] (stalled) 2014-01-09T21:49:39: Iteration 9: func=44387.4, Lambda=1, delta=0.0603885, max(grad)=4.33702 [7] 2014-01-09T21:49:39: Iteration 10: func=44387.4, Lambda=10, delta=0, max(grad)=13.2321 [7] (stalled) 2014-01-09T21:49:39: Iteration 11: func=44387.3, Lambda=1, delta=0.0520817, max(grad)=5.68515 [7] 2014-01-09T21:49:39: Iteration 12: func=44387.3, Lambda=10, delta=0, max(grad)=13.5394 [7] (stalled) 2014-01-09T21:49:39: Iteration 13: func=44387.3, Lambda=1, delta=0.0451309, max(grad)=6.77871 [7] 2014-01-09T21:49:39: Iteration 14: func=44387.3, Lambda=10, delta=0, max(grad)=13.7533 [7] (stalled) 2014-01-09T21:49:39: Iteration 15: func=44387.3, Lambda=100, delta=0, max(grad)=12.7579 [7] (stalled) 2014-01-09T21:49:39: Iteration 16: func=44387.3, Lambda=10, delta=0.00431746, max(grad)=12741.4 [6] 2014-01-09T21:49:39: Iteration 17: func=44387.3, Lambda=100, delta=0, max(grad)=0 [-1] (stalled) 2014-01-09T21:49:39: Iteration 18: func=44387.3, Lambda=1000, delta=0, max(grad)=0 [-1] (stalled) 2014-01-09T21:49:39: Iteration 19: func=44387.3, Lambda=10000, delta=0, max(grad)=0 [-1] (stalled) 2014-01-09T21:49:39: Iteration 20: func=44387.3, Lambda=100000, delta=0, max(grad)=5.46593 [7] (stalled) 2014-01-09T21:49:39: Iteration 21: func=44387.3, Lambda=1e+06, delta=0, max(grad)=6.74412 [7] (stalled) 2014-01-09T21:49:39: Iteration 22: func=44387.3, Lambda=1e+07, delta=0, max(grad)=12741.1 [6] (stalled) 2014-01-09T21:49:40: Iteration 23: func=44387.3, Lambda=1e+08, delta=0, max(grad)=12741.4 [6] (stalled) 2014-01-09T21:49:40: Iteration 24: func=44387.3, Lambda=1e+09, delta=0, max(grad)=12741.4 [6] (stalled) 2014-01-09T21:49:40: Iteration 25: func=44387.3, Lambda=1e+10, delta=0, max(grad)=12741.4 [6] (stalled) 2014-01-09T21:49:40: Iteration 26: func=44387.3, Lambda=1e+11, delta=0, max(grad)=12741.4 [6] (stalled)
Recurrence
No recurrence.
Related issues
History
#1
Updated by Knödlseder Jürgen about 10 years ago
- Priority changed from Normal to High
- Target version set to 1.0.0
This definitely should be investigate before we get the release 1.0.0 out.
#2
Updated by Knödlseder Jürgen about 10 years ago
If you follow the quickstart example from the documentation, doing an unbinned ctlike analysis produces exactly the problem:
2014-10-30T22:55:20: +=================================+ 2014-10-30T22:55:20: | Maximum likelihood optimisation | 2014-10-30T22:55:20: +=================================+ 2014-10-30T22:55:20: >Iteration 0: -logL=44395.244, Lambda=1.0e-03 2014-10-30T22:55:20: Iteration 1: -logL=44395.244, Lambda=1.0e-03, delta=-0.979, max(|grad|)=9.644743 [Sigma:6] (stalled) 2014-10-30T22:55:20: Iteration 2: -logL=44395.244, Lambda=1.0e-02, delta=-0.976, max(|grad|)=8.980893 [Sigma:6] (stalled) 2014-10-30T22:55:20: Iteration 3: -logL=44395.244, Lambda=1.0e-01, delta=-0.997, max(|grad|)=-4.897460 [Index:3] (stalled) 2014-10-30T22:55:20: >Iteration 4: -logL=44392.328, Lambda=1.0e+00, delta=2.916, max(|grad|)=-37.029192 [Sigma:6] 2014-10-30T22:55:20: Iteration 5: -logL=44392.328, Lambda=1.0e-01, delta=-3.884, max(|grad|)=8.963805 [Sigma:6] (stalled) 2014-10-30T22:55:20: >Iteration 6: -logL=44391.437, Lambda=1.0e+00, delta=0.891, max(|grad|)=-19.625468 [Sigma:6] 2014-10-30T22:55:20: Iteration 7: -logL=44391.437, Lambda=1.0e-01, delta=-4.781, max(|grad|)=11.452963 [Sigma:6] (stalled) 2014-10-30T22:55:20: Iteration 8: -logL=44391.437, Lambda=1.0e+00, delta=-4.840, max(|grad|)=13.273568 [Normalization:7] (stalled) 2014-10-30T22:55:20: >Iteration 9: -logL=44391.377, Lambda=1.0e+01, delta=0.060, max(|grad|)=-18.011138 [Sigma:6] 2014-10-30T22:55:20: Iteration 10: -logL=44391.377, Lambda=1.0e+00, delta=-4.888, max(|grad|)=13.655269 [Normalization:7] (stalled) 2014-10-30T22:55:20: >Iteration 11: -logL=44391.325, Lambda=1.0e+01, delta=0.052, max(|grad|)=-16.578132 [Sigma:6] 2014-10-30T22:55:20: Iteration 12: -logL=44391.325, Lambda=1.0e+00, delta=-4.930, max(|grad|)=13.930485 [Normalization:7] (stalled) 2014-10-30T22:55:20: >Iteration 13: -logL=44391.279, Lambda=1.0e+01, delta=0.045, max(|grad|)=-15.303160 [Sigma:6] 2014-10-30T22:55:20: Iteration 14: -logL=44391.279, Lambda=1.0e+00, delta=-4.967, max(|grad|)=14.115798 [Normalization:7] (stalled) 2014-10-30T22:55:20: Iteration 15: -logL=44391.279, Lambda=1.0e+01, delta=-5.070, max(|grad|)=13.352603 [Normalization:7] (stalled) 2014-10-30T22:55:20: >Iteration 16: -logL=44391.275, Lambda=1.0e+02, delta=0.004, max(|grad|)=-15.176887 [Sigma:6] 2014-10-30T22:55:20: 2014-10-30T22:55:20: +=========================================+ 2014-10-30T22:55:20: | Maximum likelihood optimization results | 2014-10-30T22:55:20: +=========================================+ 2014-10-30T22:55:20: === GOptimizerLM === 2014-10-30T22:55:20: Optimized function value ..: 44391.271 2014-10-30T22:55:20: Absolute precision ........: 0.005 2014-10-30T22:55:20: Optimization status .......: converged 2014-10-30T22:55:20: Number of parameters ......: 9 2014-10-30T22:55:20: Number of free parameters .: 4 2014-10-30T22:55:20: Number of iterations ......: 6 2014-10-30T22:55:20: Lambda ....................: 10 2014-10-30T22:55:20: Maximum log likelihood ....: -44391.271 2014-10-30T22:55:20: Observed events (Nobs) ...: 6127.000 2014-10-30T22:55:20: Predicted events (Npred) ..: 6120.693 (Nobs - Npred = 6.3066)
When I then changed the spectral slope from the initial value of -2.48 to -2.41 I got:
2014-10-30T22:55:43: +=================================+ 2014-10-30T22:55:43: | Maximum likelihood optimisation | 2014-10-30T22:55:43: +=================================+ 2014-10-30T22:55:43: >Iteration 0: -logL=44400.561, Lambda=1.0e-03 2014-10-30T22:55:43: >Iteration 1: -logL=44396.253, Lambda=1.0e-03, delta=4.308, max(|grad|)=9.645475 [Sigma:6] 2014-10-30T22:55:43: >Iteration 2: -logL=44396.162, Lambda=1.0e-04, delta=0.091, max(|grad|)=-0.336139 [Sigma:6] 2014-10-30T22:55:43: >Iteration 3: -logL=44396.162, Lambda=1.0e-05, delta=0.000, max(|grad|)=0.005603 [Sigma:6] 2014-10-30T22:55:43: 2014-10-30T22:55:43: +=========================================+ 2014-10-30T22:55:43: | Maximum likelihood optimization results | 2014-10-30T22:55:43: +=========================================+ 2014-10-30T22:55:43: === GOptimizerLM === 2014-10-30T22:55:43: Optimized function value ..: 44396.162 2014-10-30T22:55:43: Absolute precision ........: 0.005 2014-10-30T22:55:43: Optimization status .......: converged 2014-10-30T22:55:43: Number of parameters ......: 9 2014-10-30T22:55:43: Number of free parameters .: 4 2014-10-30T22:55:43: Number of iterations ......: 3 2014-10-30T22:55:43: Lambda ....................: 1e-06 2014-10-30T22:55:43: Maximum log likelihood ....: -44396.162 2014-10-30T22:55:43: Observed events (Nobs) ...: 6127.000 2014-10-30T22:55:43: Predicted events (Npred) ..: 6127.000 (Nobs - Npred = 0.000141861)
#3
Updated by Knödlseder Jürgen about 10 years ago
- Priority changed from High to Urgent
#4
Updated by Knödlseder Jürgen about 10 years ago
- Status changed from New to Feedback
- Assigned To set to Knödlseder Jürgen
- % Done changed from 0 to 90
Allowing to continue iterations in case of a slight log-likelihood decrease solves the issue. I did this allow already before, but the threshold was set to 1e-6, hence a very tiny decrease. Now I allow for a likelihood decrease one 1.0 which may help to get out of a local minimum that hampers convergence. I added for this purpose an accept_dec() method to GOptimizerLM that allows for a user adjustment of the threshold (by default set to 1.0). Below the ctlike results with this new approach:
2014-10-31T14:45:29: +=================================+ 2014-10-31T14:45:29: | Maximum likelihood optimisation | 2014-10-31T14:45:29: +=================================+ 2014-10-31T14:45:29: >Iteration 0: -logL=44395.244, Lambda=1.0e-03 2014-10-31T14:45:29: Iteration 1: -logL=44396.223, Lambda=1.0e-03, delta=-0.979, max(|grad|)=9.644743 [Sigma:6] (stalled) 2014-10-31T14:45:29: >Iteration 2: -logL=44396.162, Lambda=1.0e-02, delta=0.061, max(|grad|)=-0.309683 [Normalization:7] 2014-10-31T14:45:29: >Iteration 3: -logL=44396.162, Lambda=1.0e-03, delta=0.000, max(|grad|)=-0.008931 [Index:3] 2014-10-31T14:45:29: 2014-10-31T14:45:29: +=========================================+ 2014-10-31T14:45:29: | Maximum likelihood optimization results | 2014-10-31T14:45:29: +=========================================+ 2014-10-31T14:45:29: === GOptimizerLM === 2014-10-31T14:45:29: Optimized function value ..: 44396.162 2014-10-31T14:45:29: Absolute precision ........: 0.005 2014-10-31T14:45:29: Acceptable function decrease : 1 2014-10-31T14:45:29: Optimization status .......: converged 2014-10-31T14:45:29: Number of parameters ......: 9 2014-10-31T14:45:29: Number of free parameters .: 4 2014-10-31T14:45:29: Number of iterations ......: 3 2014-10-31T14:45:29: Lambda ....................: 0.0001 2014-10-31T14:45:29: Maximum log likelihood ....: -44396.162 2014-10-31T14:45:29: Observed events (Nobs) ...: 6127.000 2014-10-31T14:45:29: Predicted events (Npred) ..: 6127.000 (Nobs - Npred = 0.000303534) 2014-10-31T14:45:29: === GModels === 2014-10-31T14:45:29: Number of models ..........: 2 2014-10-31T14:45:29: Number of parameters ......: 9 2014-10-31T14:45:29: === GModelSky === 2014-10-31T14:45:29: Name ......................: Crab 2014-10-31T14:45:29: Instruments ...............: all 2014-10-31T14:45:29: Instrument scale factors ..: unity 2014-10-31T14:45:29: Observation identifiers ...: all 2014-10-31T14:45:29: Model type ................: PointSource 2014-10-31T14:45:29: Model components ..........: "SkyDirFunction" * "PowerLaw" * "Constant" 2014-10-31T14:45:29: Number of parameters ......: 6 2014-10-31T14:45:29: Number of spatial par's ...: 2 2014-10-31T14:45:29: RA .......................: 83.6331 [-360,360] deg (fixed,scale=1) 2014-10-31T14:45:29: DEC ......................: 22.0145 [-90,90] deg (fixed,scale=1) 2014-10-31T14:45:29: Number of spectral par's ..: 3 2014-10-31T14:45:29: Prefactor ................: 6.13325e-16 +/- 2.05738e-17 [1e-23,1e-13] ph/cm2/s/MeV (free,scale=1e-16,gradient) 2014-10-31T14:45:29: Index ....................: -2.5057 +/- 0.0250818 [-0,-5] (free,scale=-1,gradient) 2014-10-31T14:45:29: PivotEnergy ..............: 300000 [10000,1e+09] MeV (fixed,scale=1e+06,gradient) 2014-10-31T14:45:29: Number of temporal par's ..: 1 2014-10-31T14:45:29: Constant .................: 1 (relative value) (fixed,scale=1,gradient) 2014-10-31T14:45:29: === GCTAModelRadialAcceptance === 2014-10-31T14:45:29: Name ......................: Background 2014-10-31T14:45:29: Instruments ...............: CTA 2014-10-31T14:45:29: Instrument scale factors ..: unity 2014-10-31T14:45:29: Observation identifiers ...: all 2014-10-31T14:45:29: Model type ................: "Gaussian" * "FileFunction" * "Constant" 2014-10-31T14:45:29: Number of parameters ......: 3 2014-10-31T14:45:29: Number of radial par's ....: 1 2014-10-31T14:45:29: Sigma ....................: 3.04429 +/- 0.0330227 [0.01,10] deg2 (free,scale=1,gradient) 2014-10-31T14:45:29: Number of spectral par's ..: 1 2014-10-31T14:45:29: Normalization ............: 0.996767 +/- 0.0175227 [0,1000] (free,scale=1,gradient) 2014-10-31T14:45:29: Number of temporal par's ..: 1 2014-10-31T14:45:29: Constant .................: 1 (relative value) (fixed,scale=1,gradient) 2014-10-31T14:45:29: 2014-10-31T14:45:29: +==============+ 2014-10-31T14:45:29: | Save results | 2014-10-31T14:45:29: +==============+ 2014-10-31T14:45:29: 2014-10-31T14:45:29: Application "ctlike" terminated after 2 wall clock seconds, consuming 0.255421 seconds of CPU time.
#5
Updated by Knödlseder Jürgen about 10 years ago
Keep this under feedback to see how this new feature works in real live.
#6
Updated by Knödlseder Jürgen about 10 years ago
- % Done changed from 90 to 50
The new behavior leads to a problem when fitting the diffuse model in unbinned mode. Here the output:
+=================================================+ | Unbinned maximum likelihood fitting of CTA data | +=================================================+ >Iteration 0: -logL=32632.072, Lambda=1.0e-03 >Iteration 1: -logL=32632.022, Lambda=1.0e-03, delta=0.050, max(|grad|)=-70.083938 [Index:7] Iteration 2: -logL=32632.540, Lambda=1.0e-04, delta=-0.518, max(|grad|)=-30.223946 [Index:2] (stalled) Iteration 3: -logL=32633.289, Lambda=1.0e-03, delta=-0.749, max(|grad|)=-80.038846 [Index:7] (stalled) >Iteration 4: -logL=32631.386, Lambda=1.0e-02, delta=1.903, max(|grad|)=-21.672392 [Index:2] Iteration 5: -logL=32632.256, Lambda=1.0e-03, delta=-0.870, max(|grad|)=-63.668797 [Index:7] (stalled) >Iteration 6: -logL=32631.334, Lambda=1.0e-02, delta=0.922, max(|grad|)=-21.387280 [Index:2] Iteration 7: -logL=32632.109, Lambda=1.0e-03, delta=-0.775, max(|grad|)=-60.208218 [Index:7] (stalled) >Iteration 8: -logL=32631.328, Lambda=1.0e-02, delta=0.781, max(|grad|)=-21.375283 [Index:2] Iteration 9: -logL=32632.089, Lambda=1.0e-03, delta=-0.761, max(|grad|)=-59.682426 [Index:7] (stalled) >Iteration 10: -logL=32631.328, Lambda=1.0e-02, delta=0.761, max(|grad|)=-21.378861 [Index:2] Iteration 11: -logL=32632.086, Lambda=1.0e-03, delta=-0.759, max(|grad|)=-59.619641 [Index:7] (stalled) >Iteration 12: -logL=32631.328, Lambda=1.0e-02, delta=0.759, max(|grad|)=-21.379756 [Index:2] Iteration 13: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.613251 [Index:7] (stalled) >Iteration 14: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379921 [Index:2] Iteration 15: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612789 [Index:7] (stalled) >Iteration 16: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379945 [Index:2] Iteration 17: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612786 [Index:7] (stalled) >Iteration 18: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 19: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 20: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379948 [Index:2] Iteration 21: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612794 [Index:7] (stalled) >Iteration 22: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 23: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 24: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 25: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 26: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 27: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 28: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 29: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 30: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 31: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 32: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 33: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 34: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 35: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 36: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 37: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 38: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 39: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612798 [Index:7] (stalled) >Iteration 40: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 41: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 42: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 43: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 44: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 45: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 46: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 47: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 48: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 49: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 50: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 51: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 52: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 53: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 54: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 55: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 56: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 57: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612798 [Index:7] (stalled) >Iteration 58: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 59: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 60: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 61: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 62: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379948 [Index:2] Iteration 63: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612794 [Index:7] (stalled) >Iteration 64: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 65: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 66: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 67: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 68: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 69: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 70: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 71: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 72: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 73: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 74: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 75: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 76: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 77: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 78: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 79: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 80: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 81: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 82: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 83: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 84: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379948 [Index:2] Iteration 85: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 86: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 87: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 88: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379948 [Index:2] Iteration 89: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 90: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 91: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 92: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 93: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612797 [Index:7] (stalled) >Iteration 94: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379948 [Index:2] Iteration 95: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612794 [Index:7] (stalled) >Iteration 96: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 97: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612795 [Index:7] (stalled) >Iteration 98: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] Iteration 99: -logL=32632.086, Lambda=1.0e-03, delta=-0.758, max(|grad|)=-59.612796 [Index:7] (stalled) >Iteration 100: -logL=32631.328, Lambda=1.0e-02, delta=0.758, max(|grad|)=-21.379949 [Index:2] === GOptimizerLM === Optimized function value ..: 32631.328 Absolute precision ........: 0.005 Acceptable value decrease .: 1 Optimization status .......: converged Number of parameters ......: 10 Number of free parameters .: 5 Number of iterations ......: 101 Lambda ....................: 0.001 === GModels === Number of models ..........: 2 Number of parameters ......: 10 === GModelSky === Name ......................: Crab radio template Instruments ...............: all Instrument scale factors ..: unity Observation identifiers ...: all Model type ................: DiffuseSource Model components ..........: "SpatialMap" * "PowerLaw" * "Constant" Number of parameters ......: 5 Number of spatial par's ...: 1 Prefactor ................: 1 [0.001,1000] (fixed,scale=1,gradient) Number of spectral par's ..: 3 Prefactor ................: 5.62221e-16 +/- 1.83243e-16 [1e-23,1e-13] ph/cm2/s/MeV (free,scale=1e-16,gradient) Index ....................: -2.42874 +/- 0.197857 [-0,-5] (free,scale=-1,gradient) PivotEnergy ..............: 300000 [10000,1e+09] MeV (fixed,scale=1e+06,gradient) Number of temporal par's ..: 1 Constant .................: 1 (relative value) (fixed,scale=1,gradient) === GCTAModelRadialAcceptance === Name ......................: Background Instruments ...............: CTA Instrument scale factors ..: unity Observation identifiers ...: all Model type ................: "Gaussian" * "PowerLaw" * "Constant" Number of parameters ......: 5 Number of radial par's ....: 1 Sigma ....................: 3.03361 +/- 0.0409843 [0.01,10] deg2 (free,scale=1,gradient) Number of spectral par's ..: 3 Prefactor ................: 6.45749e-05 +/- 3.81354e-06 [0,0.001] ph/cm2/s/MeV (free,scale=1e-06,gradient) Index ....................: -1.83205 +/- 0.0330376 [-0,-5] (free,scale=-1,gradient) PivotEnergy ..............: 1e+06 [10000,1e+09] MeV (fixed,scale=1e+06,gradient) Number of temporal par's ..: 1 Constant .................: 1 (relative value) (fixed,scale=1,gradient) Elapsed time ..............: 5.560 sec
#7
Updated by Knödlseder Jürgen about 10 years ago
From iteration 13 on the algorithm enters obviously an oscillation between improvement and worsening of the logL without any net improvement in the long run.
#8
Updated by Knödlseder Jürgen about 10 years ago
- % Done changed from 50 to 100
To prevent the problem, I limited the maximum number of allowed function decreases to 3. Hope that this is well adapted to real life situations.
#9
Updated by Knödlseder Jürgen over 9 years ago
- Status changed from Feedback to Closed
So far no problems encountered anymore, close now.