Reading training examples...done
Training set properties: 19 features, 180 rankings, 15504 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=1.012667
Iter 1: .........*(NumConst=1, SV=1, CEps=1012.6667, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1051.5550, QPEps=0.0006)
Iter 3: .........*(NumConst=3, SV=3, CEps=2589.1694, QPEps=0.0033)
Iter 4: .........*(NumConst=4, SV=4, CEps=2064.7546, QPEps=0.0049)
Iter 5: .........*(NumConst=5, SV=4, CEps=1567.0629, QPEps=0.0001)
Iter 6: .........*(NumConst=6, SV=5, CEps=1513.9181, QPEps=0.0005)
Iter 7: .........*(NumConst=7, SV=5, CEps=921.9484, QPEps=16.6649)
Iter 8: .........*(NumConst=8, SV=7, CEps=584.0681, QPEps=6.4601)
Iter 9: .........*(NumConst=9, SV=8, CEps=340.0668, QPEps=0.0005)
Iter 10: .........*(NumConst=10, SV=8, CEps=430.5753, QPEps=47.5318)
Iter 11: .........*(NumConst=11, SV=7, CEps=204.0526, QPEps=44.7253)
Iter 12: .........*(NumConst=12, SV=9, CEps=166.6096, QPEps=37.9039)
Iter 13: .........*(NumConst=13, SV=7, CEps=251.4047, QPEps=18.0624)
Iter 14: .........*(NumConst=14, SV=7, CEps=233.1640, QPEps=1.7373)
Iter 15: .........*(NumConst=15, SV=7, CEps=159.5358, QPEps=3.5096)
Iter 16: .........*(NumConst=16, SV=7, CEps=164.6639, QPEps=43.2112)
Iter 17: .........*(NumConst=17, SV=8, CEps=93.0291, QPEps=11.1295)
Iter 18: .........*(NumConst=18, SV=7, CEps=76.7732, QPEps=4.9667)
Iter 19: .........*(NumConst=19, SV=7, CEps=82.6912, QPEps=0.5280)
Iter 20: .........*(NumConst=20, SV=8, CEps=43.2207, QPEps=18.0693)
Iter 21: .........*(NumConst=21, SV=7, CEps=73.1820, QPEps=15.1237)
Iter 22: .........*(NumConst=22, SV=7, CEps=59.1550, QPEps=8.3699)
Iter 23: .........*(NumConst=23, SV=7, CEps=45.8839, QPEps=0.1211)
Iter 24: .........*(NumConst=24, SV=7, CEps=36.8684, QPEps=17.1360)
Iter 25: .........*(NumConst=25, SV=7, CEps=35.1198, QPEps=6.0947)
Iter 26: .........*(NumConst=26, SV=7, CEps=34.3032, QPEps=11.3083)
Iter 27: .........*(NumConst=27, SV=9, CEps=22.8680, QPEps=7.5080)
Iter 28: .........*(NumConst=28, SV=7, CEps=19.5888, QPEps=3.8636)
Iter 29: .........*(NumConst=29, SV=8, CEps=18.9656, QPEps=7.8697)
Iter 30: .........*(NumConst=30, SV=7, CEps=27.2521, QPEps=0.0000)
Iter 31: .........*(NumConst=31, SV=10, CEps=13.7106, QPEps=5.8672)
Iter 32: .........*(NumConst=32, SV=8, CEps=17.7723, QPEps=4.2767)
Iter 33: .........*(NumConst=33, SV=10, CEps=12.3250, QPEps=0.1809)
Iter 34: .........*(NumConst=34, SV=8, CEps=15.0432, QPEps=2.5252)
Iter 35: .........*(NumConst=35, SV=8, CEps=12.4142, QPEps=0.5978)
Iter 36: .........*(NumConst=36, SV=8, CEps=11.6352, QPEps=0.0001)
Iter 37: .........*(NumConst=37, SV=8, CEps=8.9377, QPEps=0.0429)
Iter 38: .........*(NumConst=38, SV=9, CEps=7.6225, QPEps=0.0000)
Iter 39: .........*(NumConst=39, SV=7, CEps=7.1671, QPEps=1.4466)
Iter 40: .........*(NumConst=40, SV=7, CEps=5.1937, QPEps=0.0001)
Iter 41: .........*(NumConst=41, SV=8, CEps=6.4950, QPEps=2.3432)
Iter 42: .........*(NumConst=42, SV=10, CEps=4.6307, QPEps=1.6398)
Iter 43: .........*(NumConst=43, SV=7, CEps=5.0913, QPEps=2.0424)
Iter 44: .........*(NumConst=44, SV=8, CEps=4.5188, QPEps=0.4491)
Iter 45: .........*(NumConst=45, SV=9, CEps=3.9719, QPEps=0.0000)
Iter 46: .........*(NumConst=46, SV=9, CEps=3.9368, QPEps=1.7153)
Iter 47: .........*(NumConst=47, SV=8, CEps=2.2829, QPEps=0.0002)
Iter 48: .........*(NumConst=48, SV=8, CEps=2.9114, QPEps=0.0000)
Iter 49: .........*(NumConst=49, SV=9, CEps=2.6022, QPEps=0.7738)
Iter 50: .........*(NumConst=50, SV=8, CEps=3.5741, QPEps=0.0000)
Iter 51: .........*(NumConst=51, SV=7, CEps=1.5929, QPEps=0.2845)
Iter 52: .........*(NumConst=52, SV=8, CEps=4.2131, QPEps=0.0723)
Iter 53: .........*(NumConst=53, SV=7, CEps=2.0628, QPEps=0.0581)
Iter 54: .........*(NumConst=54, SV=7, CEps=1.8475, QPEps=0.5587)
Iter 55: .........*(NumConst=55, SV=8, CEps=1.2096, QPEps=0.3638)
Iter 56: .........*(NumConst=55, SV=8, CEps=1.5409, QPEps=0.0000)
Iter 57: .........*(NumConst=56, SV=8, CEps=1.0975, QPEps=0.4965)
Iter 58: .........*(NumConst=57, SV=8, CEps=1.3669, QPEps=0.0045)
Iter 59: .........(NumConst=57, SV=8, CEps=0.8076, QPEps=0.0045)
Final epsilon on KKT-Conditions: 0.80765
Upper bound on duality gap: 0.04034
Dual objective value: dval=33.29422
Primal objective value: pval=33.33456
Total number of constraints in final working set: 57 (of 58)
Number of iterations: 59
Number of calls to 'find_most_violated_constraint': 10620
Number of SV: 8 
Norm of weight vector: |w|=1.33789
Value of slack variable (on working set): xi=647.98744
Value of slack variable (global): xi=648.79149
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3661.39908
Runtime in cpu-seconds: 0.24
Compacting linear model...done
Writing learned model...done