Reading training examples...done
Training set properties: 23 features, 180 rankings, 15470 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.975389
Iter 1: .........*(NumConst=1, SV=1, CEps=975.3889, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=991.9918, QPEps=0.0008)
Iter 3: .........*(NumConst=3, SV=3, CEps=2434.1218, QPEps=0.0027)
Iter 4: .........*(NumConst=4, SV=4, CEps=6213.2104, QPEps=0.0058)
Iter 5: .........*(NumConst=5, SV=5, CEps=1047.1542, QPEps=0.0013)
Iter 6: .........*(NumConst=6, SV=5, CEps=2005.7857, QPEps=0.0042)
Iter 7: .........*(NumConst=7, SV=6, CEps=1392.4839, QPEps=48.9641)
Iter 8: .........*(NumConst=8, SV=6, CEps=2183.0576, QPEps=49.8725)
Iter 9: .........*(NumConst=9, SV=6, CEps=10401.2692, QPEps=43.0988)
Iter 10: .........*(NumConst=10, SV=7, CEps=2587.0586, QPEps=48.1623)
Iter 11: .........*(NumConst=11, SV=8, CEps=276.0322, QPEps=48.5779)
Iter 12: .........*(NumConst=12, SV=9, CEps=273.1627, QPEps=48.3402)
Iter 13: .........*(NumConst=13, SV=8, CEps=247.0827, QPEps=44.1807)
Iter 14: .........*(NumConst=14, SV=8, CEps=156.7205, QPEps=44.2516)
Iter 15: .........*(NumConst=15, SV=8, CEps=151.0400, QPEps=48.4302)
Iter 16: .........*(NumConst=16, SV=8, CEps=151.4750, QPEps=49.7801)
Iter 17: .........*(NumConst=17, SV=9, CEps=106.6000, QPEps=49.2175)
Iter 18: .........*(NumConst=18, SV=8, CEps=136.0847, QPEps=40.2645)
Iter 19: .........*(NumConst=19, SV=7, CEps=114.4596, QPEps=26.9809)
Iter 20: .........*(NumConst=20, SV=8, CEps=38.6896, QPEps=15.3813)
Iter 21: .........*(NumConst=21, SV=7, CEps=83.9709, QPEps=18.0097)
Iter 22: .........*(NumConst=22, SV=6, CEps=48.1995, QPEps=0.0000)
Iter 23: .........*(NumConst=23, SV=8, CEps=29.4450, QPEps=10.5338)
Iter 24: .........*(NumConst=24, SV=8, CEps=42.1342, QPEps=11.7386)
Iter 25: .........*(NumConst=25, SV=7, CEps=32.1137, QPEps=11.3791)
Iter 26: .........*(NumConst=26, SV=7, CEps=24.9411, QPEps=10.6896)
Iter 27: .........*(NumConst=27, SV=8, CEps=27.1543, QPEps=12.2312)
Iter 28: .........*(NumConst=28, SV=7, CEps=45.0225, QPEps=9.4676)
Iter 29: .........*(NumConst=29, SV=7, CEps=55.0282, QPEps=1.2600)
Iter 30: .........*(NumConst=30, SV=8, CEps=15.7716, QPEps=1.7188)
Iter 31: .........*(NumConst=31, SV=8, CEps=18.1336, QPEps=7.5805)
Iter 32: .........*(NumConst=32, SV=10, CEps=14.7751, QPEps=5.7083)
Iter 33: .........*(NumConst=33, SV=8, CEps=23.6629, QPEps=5.6095)
Iter 34: .........*(NumConst=34, SV=9, CEps=12.4894, QPEps=6.1218)
Iter 35: .........*(NumConst=35, SV=9, CEps=9.6297, QPEps=3.4892)
Iter 36: .........*(NumConst=36, SV=8, CEps=10.8875, QPEps=4.0948)
Iter 37: .........*(NumConst=37, SV=9, CEps=13.6686, QPEps=4.0754)
Iter 38: .........*(NumConst=38, SV=10, CEps=4.9591, QPEps=2.3513)
Iter 39: .........*(NumConst=39, SV=8, CEps=10.2855, QPEps=2.2114)
Iter 40: .........*(NumConst=40, SV=8, CEps=5.7296, QPEps=2.4273)
Iter 41: .........*(NumConst=41, SV=11, CEps=7.0518, QPEps=2.0891)
Iter 42: .........*(NumConst=42, SV=8, CEps=6.3326, QPEps=2.4594)
Iter 43: .........*(NumConst=43, SV=7, CEps=4.7853, QPEps=2.0234)
Iter 44: .........*(NumConst=44, SV=8, CEps=5.8293, QPEps=1.2960)
Iter 45: .........*(NumConst=45, SV=9, CEps=2.2859, QPEps=1.0959)
Iter 46: .........*(NumConst=46, SV=9, CEps=6.1569, QPEps=0.7753)
Iter 47: .........*(NumConst=47, SV=9, CEps=5.7887, QPEps=0.9395)
Iter 48: .........*(NumConst=48, SV=9, CEps=2.8667, QPEps=0.9988)
Iter 49: .........*(NumConst=49, SV=7, CEps=3.0451, QPEps=1.0221)
Iter 50: .........*(NumConst=50, SV=9, CEps=2.4898, QPEps=0.7979)
Iter 51: .........*(NumConst=51, SV=8, CEps=3.4718, QPEps=1.0358)
Iter 52: .........*(NumConst=52, SV=9, CEps=1.9868, QPEps=0.8139)
Iter 53: .........*(NumConst=53, SV=10, CEps=1.6216, QPEps=0.7942)
Iter 54: .........*(NumConst=54, SV=9, CEps=2.0371, QPEps=0.4935)
Iter 55: .........*(NumConst=55, SV=8, CEps=1.1913, QPEps=0.5750)
Iter 56: .........*(NumConst=56, SV=8, CEps=1.0091, QPEps=0.4845)
Iter 57: .........*(NumConst=57, SV=12, CEps=1.1104, QPEps=0.4907)
Iter 58: .........*(NumConst=58, SV=9, CEps=0.9833, QPEps=0.4203)
Iter 59: .........(NumConst=58, SV=9, CEps=0.6575, QPEps=0.4203)
Final epsilon on KKT-Conditions: 0.65755
Upper bound on duality gap: 0.29343
Dual objective value: dval=305.30389
Primal objective value: pval=305.59732
Total number of constraints in final working set: 58 (of 58)
Number of iterations: 59
Number of calls to 'find_most_violated_constraint': 10620
Number of SV: 9 
Norm of weight vector: |w|=1.53466
Value of slack variable (on working set): xi=608.50352
Value of slack variable (global): xi=608.83946
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=5313.53177
Runtime in cpu-seconds: 5.26
Compacting linear model...done
Writing learned model...done