[Wannier] the convergence history
Jian-Xin Zhu
jxzhu at lanl.gov
Tue Aug 10 23:32:00 CEST 2010
Dear Jonathan and Respectful Wannier Users,
I notice that only very tiny difference in the input data files, case.amn and case.mmn (but with the same case.eig) can
cause very different convergence history as listed by
"grep CONV case.wout".
Specifically, in one run, we have the data in the case.amn like
....
1 1 1 0.21855E-15 -0.79635E-15
2 1 1 -0.11349E-15 -0.52089E-15
3 1 1 -0.28493E-16 -0.16306E-15
4 1 1 -0.42721E-16 -0.10135E-14
5 1 1 0.81503E-16 -0.27408E-15
6 1 1 -0.54509E-03 -0.73939E-15
7 1 1 -0.42419E-16 0.41318E-15
8 1 1 0.40974E-15 -0.12084E-15
9 1 1 0.38064E-15 -0.80970E-15
10 1 1 -0.22009E-16 -0.40603E-15
11 1 1 -0.27930E-15 -0.46414E-01
12 1 1 -0.32501E-14 0.39493E-16
....
In another run, I have the data in the case.amn like
...
1 1 1 0.21875E-15 -0.79355E-15
2 1 1 -0.11628E-15 -0.52274E-15
3 1 1 -0.28217E-16 -0.16340E-15
4 1 1 -0.42630E-16 -0.10156E-14
5 1 1 0.79738E-16 -0.27577E-15
6 1 1 -0.54509E-03 -0.73893E-15
7 1 1 -0.44166E-16 0.41235E-15
8 1 1 0.41158E-15 -0.11671E-15
9 1 1 0.38126E-15 -0.80936E-15
10 1 1 -0.11113E-16 -0.40590E-15
11 1 1 -0.28054E-15 -0.46414E-01
12 1 1 -0.32498E-14 0.36981E-16
...
As you can see, the data difference between these two files are tiny.
However, the run of wannier90.x gives (grep CONV case.wout) gives
+--------------------------------------------------------------------+<-- CONV
| Iter Delta Spread RMS Gradient Spread (Ang^2) Time |<-- CONV
+--------------------------------------------------------------------+<-- CONV
0 0.357E+04 0.0000000000 3574.7401278762 92.66 <-- CONV
1 -0.493E+02 372.8088221177 3525.4625204024 92.76 <-- CONV
500 -0.957E-01 38.9905858488 1348.7278590515 134.80 <-- CONV
1000 -0.112E+00 55.1124159940 1311.6807047511 177.57 <-- CONV
1500 -0.306E-01 36.6550468502 1278.6852681732 222.40 <-- CONV
2000 -0.625E+00 106.3566725318 1248.7507531831 266.95 <-- CONV
2500 -0.125E-01 62.4624116187 1218.6192345383 309.60 <-- CONV
3000 -0.260E+00 114.3061636811 1191.8569753522 356.11 <-- CONV
3500 0.146E-01 43.8167232573 1163.2760493524 394.46 <-- CONV
4000 -0.424E-01 46.1758663463 1135.7723168118 438.76 <-- CONV
4500 -0.345E-01 23.4327788326 1111.1772965742 482.89 <-- CONV
5000 -0.724E-01 46.7322360924 1084.2827740060 526.44 <-- CONV
5500 -0.170E+00 67.0071332476 1060.5027739486 571.39 <-- CONV
6000 -0.535E-01 150.6865621245 1038.5642711422 616.51 <-- CONV
6500 -0.862E-01 19.9912842119 1016.7446424023 657.97 <-- CONV
7000 -0.811E-01 39.7792227180 996.6408035943 701.06 <-- CONV
7500 0.857E-01 93.6613012482 977.7475983733 744.92 <-- CONV
8000 -0.578E-01 48.6060403982 959.6156229533 781.04 <-- CONV
8500 -0.471E-01 41.1839284669 942.1960667836 820.66 <-- CONV
9000 -0.390E-01 28.1378337551 925.1056291369 858.82 <-- CONV
9500 -0.239E-01 27.3326672550 908.8554497941 898.98 <-- CONV
10000 -0.143E+00 39.7871121495 893.2781557350 938.38 <-- CONV
10500 -0.162E-01 49.8551428996 876.7572784912 976.29 <-- CONV
11000 -0.681E-01 32.6137480644 862.1100683519 1015.70 <-- CONV
11500 -0.749E-01 49.9323143298 847.0265627311 1054.18 <-- CONV
12000 -0.560E-01 46.6772830218 832.9954924529 1091.03 <-- CONV
12500 -0.172E-01 47.0582924674 817.7654849282 1129.00 <-- CONV
13000 -0.104E+00 48.8424639940 802.9794526713 1166.37 <-- CONV
13500 -0.381E-01 35.5571199222 789.9775183549 1203.55 <-- CONV
14000 -0.335E-01 30.3181017578 772.8621971211 1243.37 <-- CONV
14500 -0.331E-01 57.4200424615 757.4427096017 1281.57 <-- CONV
15000 -0.413E-01 38.7877152333 740.9815686646 1316.62 <-- CONV
15500 0.107E+01 181.0717144291 727.2780760529 1355.13 <-- CONV
16000 0.113E+00 88.5568630907 709.6032889314 1393.87 <-- CONV
16500 0.311E+00 115.1199039272 694.4003946711 1428.52 <-- CONV
...
for the first set of input data files, while
+--------------------------------------------------------------------+<-- CONV
| Iter Delta Spread RMS Gradient Spread (Ang^2) Time |<-- CONV
+--------------------------------------------------------------------+<-- CONV
0 0.356E+04 0.0000000000 3558.5096736256 105.90 <-- CONV
1 -0.353E+02 423.1924704930 3523.2543928483 105.97 <-- CONV
500 -0.183E+00 52.0555974996 1368.7624483647 138.00 <-- CONV
1000 -0.426E+00 49.2248689866 1314.3325401227 167.40 <-- CONV
1500 -0.942E-01 60.0357243128 1271.6743198737 199.93 <-- CONV
2000 -0.107E+00 86.3832077848 1231.8503525761 234.03 <-- CONV
2500 -0.475E-01 65.3158979743 1186.7676497831 267.74 <-- CONV
3000 -0.116E+00 45.0657395263 1147.6577016478 296.57 <-- CONV
3500 -0.128E+00 65.7423110503 1109.7870721959 329.83 <-- CONV
4000 -0.202E+00 52.0755019227 1068.0526809933 372.79 <-- CONV
4500 -0.858E-01 48.6574279835 1026.3033625983 415.30 <-- CONV
5000 -0.604E-01 60.7029969533 985.0807189880 458.55 <-- CONV
5500 -0.699E-02 59.1745341120 938.4155415161 500.99 <-- CONV
6000 -0.597E-01 36.1744129850 884.9888932668 547.49 <-- CONV
6500 -0.184E+00 34.0572753280 835.5378730437 592.42 <-- CONV
7000 -0.124E+00 19.2000187770 763.5971483380 637.29 <-- CONV
7500 -0.264E-02 8.2876092399 324.3235536167 676.07 <-- CONV
8000 -0.214E-02 2.1559059571 322.3922244461 720.51 <-- CONV
8500 -0.707E+00 216.6533984616 322.3013601017 766.37 <-- CONV
9000 -0.570E-03 1.0489618008 321.6195647738 811.24 <-- CONV
9500 -0.476E-04 0.9413049558 319.6817585389 855.78 <-- CONV
10000 -0.437E-04 0.8011913893 318.6634661373 900.74 <-- CONV
10500 -0.548E-04 0.3515922329 317.9155341388 944.53 <-- CONV
11000 -0.104E-03 0.3515895381 317.3109500312 988.66 <-- CONV
11500 -0.152E+00 1.9180527710 317.2340508647 1034.44 <-- CONV
12000 -0.202E-02 0.8983133603 316.5451301009 1073.09 <-- CONV
12500 -0.778E-03 0.7619122497 315.9566736906 1111.86 <-- CONV
13000 -0.145E-02 1.2927764041 315.7531082220 1151.68 <-- CONV
13500 -0.527E-03 0.8385362060 315.6372320068 1191.63 <-- CONV
14000 -0.712E-03 0.2385556039 313.9009178869 1231.34 <-- CONV
14500 -0.769E-02 0.7448840127 313.6252928788 1270.74 <-- CONV
15000 -0.302E+00 7.5948125445 314.9248903246 1308.39 <-- CONV
15500 -0.109E-05 0.9375040298 313.3253683847 1346.33 <-- CONV
16000 -0.167E-06 0.6625200904 313.3251390354 1385.34 <-- CONV
16500 -0.707E-07 0.5599569354 313.3250736131 1424.91 <-- CONV
...
for the second set of input data files.
Is this kind of difference in the convergence history acceptable?
In the case.win, I put in trial_step = 0.5, but is such a large number of iterations
(at the level of several ten thousands) needed for convergence
really making sense?
Thanks for the comment.
Jianxin
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