Reading training examples...done
Training set properties: 23 features, 180 rankings, 15555 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=1.003494
Iter 1: .........*(NumConst=1, SV=1, CEps=1003.4944, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1036.5524, QPEps=0.0010)
Iter 3: .........*(NumConst=3, SV=3, CEps=3276.6389, QPEps=0.0011)
Iter 4: .........*(NumConst=4, SV=4, CEps=3161.1944, QPEps=0.0066)
Iter 5: .........*(NumConst=5, SV=5, CEps=2970.5984, QPEps=0.0023)
Iter 6: .........*(NumConst=6, SV=5, CEps=1709.5789, QPEps=0.0014)
Iter 7: .........*(NumConst=7, SV=6, CEps=1520.1188, QPEps=42.1793)
Iter 8: .........*(NumConst=8, SV=6, CEps=1025.7097, QPEps=43.5160)
Iter 9: .........*(NumConst=9, SV=8, CEps=438.3065, QPEps=40.7410)
Iter 10: .........*(NumConst=10, SV=7, CEps=265.3260, QPEps=36.4450)
Iter 11: .........*(NumConst=11, SV=7, CEps=201.7730, QPEps=38.0151)
Iter 12: .........*(NumConst=12, SV=7, CEps=239.2643, QPEps=22.9708)
Iter 13: .........*(NumConst=13, SV=7, CEps=213.1821, QPEps=23.6858)
Iter 14: .........*(NumConst=14, SV=7, CEps=121.6167, QPEps=27.7603)
Iter 15: .........*(NumConst=15, SV=8, CEps=210.2081, QPEps=42.4850)
Iter 16: .........*(NumConst=16, SV=6, CEps=144.2593, QPEps=0.0000)
Iter 17: .........*(NumConst=17, SV=7, CEps=49.8605, QPEps=19.9406)
Iter 18: .........*(NumConst=18, SV=6, CEps=96.6752, QPEps=0.0000)
Iter 19: .........*(NumConst=19, SV=6, CEps=53.7913, QPEps=24.7055)
Iter 20: .........*(NumConst=20, SV=6, CEps=71.3464, QPEps=0.0000)
Iter 21: .........*(NumConst=21, SV=6, CEps=31.3901, QPEps=2.7663)
Iter 22: .........*(NumConst=22, SV=6, CEps=31.3335, QPEps=0.0000)
Iter 23: .........*(NumConst=23, SV=6, CEps=29.5543, QPEps=0.0000)
Iter 24: .........*(NumConst=24, SV=6, CEps=15.3408, QPEps=6.0250)
Iter 25: .........*(NumConst=25, SV=6, CEps=23.7556, QPEps=0.0000)
Iter 26: .........*(NumConst=26, SV=6, CEps=12.8789, QPEps=0.0000)
Iter 27: .........*(NumConst=27, SV=5, CEps=13.9555, QPEps=0.0000)
Iter 28: .........*(NumConst=28, SV=6, CEps=9.0535, QPEps=0.1431)
Iter 29: .........*(NumConst=29, SV=7, CEps=8.9185, QPEps=1.9750)
Iter 30: .........*(NumConst=30, SV=6, CEps=6.2581, QPEps=0.0000)
Iter 31: .........*(NumConst=31, SV=7, CEps=6.1336, QPEps=2.7888)
Iter 32: .........*(NumConst=32, SV=6, CEps=7.0024, QPEps=1.8385)
Iter 33: .........*(NumConst=33, SV=7, CEps=5.7014, QPEps=2.2484)
Iter 34: .........*(NumConst=34, SV=8, CEps=2.1388, QPEps=0.7211)
Iter 35: .........*(NumConst=35, SV=9, CEps=3.5356, QPEps=1.0415)
Iter 36: .........*(NumConst=36, SV=8, CEps=4.5215, QPEps=1.0337)
Iter 37: .........*(NumConst=37, SV=7, CEps=3.5936, QPEps=0.9876)
Iter 38: .........*(NumConst=38, SV=8, CEps=2.0981, QPEps=0.1546)
Iter 39: .........*(NumConst=39, SV=7, CEps=2.0543, QPEps=0.8924)
Iter 40: .........*(NumConst=40, SV=7, CEps=1.3772, QPEps=0.1385)
Iter 41: .........*(NumConst=41, SV=6, CEps=1.8172, QPEps=0.0000)
Iter 42: .........*(NumConst=42, SV=6, CEps=1.1073, QPEps=0.1073)
Iter 43: .........(NumConst=42, SV=6, CEps=1.0032, QPEps=0.1073)
Final epsilon on KKT-Conditions: 1.00316
Upper bound on duality gap: 0.07076
Dual objective value: dval=45.60645
Primal objective value: pval=45.67721
Total number of constraints in final working set: 42 (of 42)
Number of iterations: 43
Number of calls to 'find_most_violated_constraint': 7740
Number of SV: 6 
Norm of weight vector: |w|=1.25469
Value of slack variable (on working set): xi=640.28410
Value of slack variable (global): xi=641.28700
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=5410.24333
Runtime in cpu-seconds: 0.30
Compacting linear model...done
Writing learned model...done