Reading training examples...done
Training set properties: 25 features, 180 rankings, 15564 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=1.036789
Iter 1: .........*(NumConst=1, SV=1, CEps=1036.7889, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=780.4032, QPEps=0.0002)
Iter 3: .........*(NumConst=3, SV=2, CEps=1415.5633, QPEps=0.0005)
Iter 4: .........*(NumConst=4, SV=3, CEps=1523.9489, QPEps=0.0045)
Iter 5: .........*(NumConst=5, SV=4, CEps=2365.9443, QPEps=0.0118)
Iter 6: .........*(NumConst=6, SV=4, CEps=2335.5407, QPEps=10.1292)
Iter 7: .........*(NumConst=7, SV=5, CEps=477.0516, QPEps=0.1227)
Iter 8: .........*(NumConst=8, SV=5, CEps=540.9995, QPEps=1.3376)
Iter 9: .........*(NumConst=9, SV=6, CEps=291.3594, QPEps=0.0060)
Iter 10: .........*(NumConst=10, SV=7, CEps=225.5475, QPEps=49.8992)
Iter 11: .........*(NumConst=11, SV=7, CEps=187.2595, QPEps=11.5864)
Iter 12: .........*(NumConst=12, SV=7, CEps=239.7912, QPEps=0.0614)
Iter 13: .........*(NumConst=13, SV=6, CEps=164.4616, QPEps=1.7544)
Iter 14: .........*(NumConst=14, SV=7, CEps=227.0758, QPEps=9.1946)
Iter 15: .........*(NumConst=15, SV=7, CEps=221.2967, QPEps=48.4811)
Iter 16: .........*(NumConst=16, SV=7, CEps=126.9367, QPEps=43.7227)
Iter 17: .........*(NumConst=17, SV=9, CEps=85.6601, QPEps=26.5144)
Iter 18: .........*(NumConst=18, SV=8, CEps=83.5926, QPEps=0.3311)
Iter 19: .........*(NumConst=19, SV=7, CEps=107.7108, QPEps=0.0061)
Iter 20: .........*(NumConst=20, SV=7, CEps=66.7266, QPEps=2.1200)
Iter 21: .........*(NumConst=21, SV=6, CEps=51.8727, QPEps=14.7641)
Iter 22: .........*(NumConst=22, SV=8, CEps=39.0517, QPEps=1.9353)
Iter 23: .........*(NumConst=23, SV=7, CEps=63.6335, QPEps=4.5562)
Iter 24: .........*(NumConst=24, SV=7, CEps=57.8750, QPEps=3.8682)
Iter 25: .........*(NumConst=25, SV=10, CEps=31.7873, QPEps=10.7031)
Iter 26: .........*(NumConst=26, SV=8, CEps=52.5988, QPEps=15.7929)
Iter 27: .........*(NumConst=27, SV=6, CEps=29.6402, QPEps=0.4735)
Iter 28: .........*(NumConst=28, SV=7, CEps=26.2804, QPEps=8.8179)
Iter 29: .........*(NumConst=29, SV=7, CEps=24.6188, QPEps=9.2781)
Iter 30: .........*(NumConst=30, SV=7, CEps=22.7915, QPEps=10.8248)
Iter 31: .........*(NumConst=31, SV=7, CEps=18.7585, QPEps=7.9165)
Iter 32: .........*(NumConst=32, SV=8, CEps=34.5159, QPEps=6.2726)
Iter 33: .........*(NumConst=33, SV=7, CEps=19.1263, QPEps=9.0621)
Iter 34: .........*(NumConst=34, SV=8, CEps=17.0186, QPEps=2.4527)
Iter 35: .........*(NumConst=35, SV=9, CEps=15.7174, QPEps=6.1707)
Iter 36: .........*(NumConst=36, SV=7, CEps=24.5982, QPEps=0.1192)
Iter 37: .........*(NumConst=37, SV=7, CEps=9.0559, QPEps=4.2564)
Iter 38: .........*(NumConst=38, SV=7, CEps=8.1163, QPEps=1.1057)
Iter 39: .........*(NumConst=39, SV=7, CEps=13.8179, QPEps=3.9664)
Iter 40: .........*(NumConst=40, SV=8, CEps=7.6974, QPEps=3.4467)
Iter 41: .........*(NumConst=41, SV=7, CEps=11.3170, QPEps=3.8226)
Iter 42: .........*(NumConst=42, SV=8, CEps=6.0094, QPEps=2.9815)
Iter 43: .........*(NumConst=43, SV=8, CEps=10.2208, QPEps=2.9735)
Iter 44: .........*(NumConst=44, SV=6, CEps=4.3753, QPEps=0.7365)
Iter 45: .........*(NumConst=45, SV=7, CEps=3.9692, QPEps=0.4881)
Iter 46: .........*(NumConst=46, SV=7, CEps=3.2662, QPEps=0.3310)
Iter 47: .........*(NumConst=47, SV=7, CEps=3.1842, QPEps=0.0616)
Iter 48: .........*(NumConst=48, SV=7, CEps=2.2179, QPEps=0.0000)
Iter 49: .........*(NumConst=49, SV=7, CEps=2.4984, QPEps=0.7862)
Iter 50: .........*(NumConst=50, SV=7, CEps=2.3583, QPEps=0.0994)
Iter 51: .........*(NumConst=51, SV=9, CEps=2.1169, QPEps=0.4895)
Iter 52: .........*(NumConst=52, SV=10, CEps=1.5924, QPEps=0.7852)
Iter 53: .........*(NumConst=53, SV=9, CEps=1.6285, QPEps=0.7090)
Iter 54: .........*(NumConst=53, SV=9, CEps=2.1037, QPEps=0.6937)
Iter 55: .........*(NumConst=54, SV=10, CEps=1.3465, QPEps=0.4151)
Iter 56: .........*(NumConst=54, SV=9, CEps=1.5587, QPEps=0.6594)
Iter 57: .........*(NumConst=54, SV=8, CEps=1.1634, QPEps=0.5774)
Iter 58: .........*(NumConst=55, SV=8, CEps=1.1254, QPEps=0.0017)
Iter 59: .........(NumConst=55, SV=8, CEps=0.8077, QPEps=0.0017)
Final epsilon on KKT-Conditions: 0.80770
Upper bound on duality gap: 0.04040
Dual objective value: dval=31.26306
Primal objective value: pval=31.30346
Total number of constraints in final working set: 55 (of 58)
Number of iterations: 59
Number of calls to 'find_most_violated_constraint': 10620
Number of SV: 8 
Norm of weight vector: |w|=0.79128
Value of slack variable (on working set): xi=619.00051
Value of slack variable (global): xi=619.80802
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=6591.73320
Runtime in cpu-seconds: 0.86
Compacting linear model...done
Writing learned model...done