Reading training examples...done
Training set properties: 49 features, 180 rankings, 15506 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.986667
Iter 1: .........*(NumConst=1, SV=1, CEps=986.6667, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1676.1243, QPEps=0.0001)
Iter 3: .........*(NumConst=3, SV=3, CEps=1483.9652, QPEps=0.0004)
Iter 4: .........*(NumConst=4, SV=4, CEps=1537.4720, QPEps=0.0018)
Iter 5: .........*(NumConst=5, SV=4, CEps=1114.6552, QPEps=0.0007)
Iter 6: .........*(NumConst=6, SV=5, CEps=581.1379, QPEps=0.0043)
Iter 7: .........*(NumConst=7, SV=6, CEps=406.1005, QPEps=0.0014)
Iter 8: .........*(NumConst=8, SV=6, CEps=382.4007, QPEps=10.5275)
Iter 9: .........*(NumConst=9, SV=6, CEps=264.7071, QPEps=4.8339)
Iter 10: .........*(NumConst=10, SV=7, CEps=198.9667, QPEps=9.4407)
Iter 11: .........*(NumConst=11, SV=6, CEps=135.5058, QPEps=19.6022)
Iter 12: .........*(NumConst=12, SV=5, CEps=178.0755, QPEps=17.9816)
Iter 13: .........*(NumConst=13, SV=6, CEps=170.3718, QPEps=31.1642)
Iter 14: .........*(NumConst=14, SV=6, CEps=98.2440, QPEps=5.3043)
Iter 15: .........*(NumConst=15, SV=6, CEps=81.4086, QPEps=24.3513)
Iter 16: .........*(NumConst=16, SV=7, CEps=80.6680, QPEps=5.8732)
Iter 17: .........*(NumConst=17, SV=7, CEps=59.4501, QPEps=22.2945)
Iter 18: .........*(NumConst=18, SV=8, CEps=57.8443, QPEps=0.0011)
Iter 19: .........*(NumConst=19, SV=9, CEps=45.3578, QPEps=9.4957)
Iter 20: .........*(NumConst=20, SV=7, CEps=55.3171, QPEps=13.9640)
Iter 21: .........*(NumConst=21, SV=8, CEps=35.5647, QPEps=4.5249)
Iter 22: .........*(NumConst=22, SV=7, CEps=28.5153, QPEps=0.1004)
Iter 23: .........*(NumConst=23, SV=7, CEps=20.6535, QPEps=2.6626)
Iter 24: .........*(NumConst=24, SV=6, CEps=35.7814, QPEps=4.4647)
Iter 25: .........*(NumConst=25, SV=6, CEps=16.1647, QPEps=2.9733)
Iter 26: .........*(NumConst=26, SV=7, CEps=19.9392, QPEps=0.0025)
Iter 27: .........*(NumConst=27, SV=6, CEps=16.7496, QPEps=2.0924)
Iter 28: .........*(NumConst=28, SV=7, CEps=12.8903, QPEps=6.3339)
Iter 29: .........*(NumConst=29, SV=8, CEps=7.9150, QPEps=3.5103)
Iter 30: .........*(NumConst=30, SV=5, CEps=13.2683, QPEps=3.2188)
Iter 31: .........*(NumConst=31, SV=6, CEps=6.7772, QPEps=0.9719)
Iter 32: .........*(NumConst=32, SV=7, CEps=5.9382, QPEps=0.0216)
Iter 33: .........*(NumConst=33, SV=7, CEps=5.1070, QPEps=2.2572)
Iter 34: .........*(NumConst=34, SV=7, CEps=10.7022, QPEps=1.7829)
Iter 35: .........*(NumConst=35, SV=8, CEps=5.1324, QPEps=0.0002)
Iter 36: .........*(NumConst=36, SV=7, CEps=7.1847, QPEps=0.0001)
Iter 37: .........*(NumConst=37, SV=7, CEps=3.5808, QPEps=0.2297)
Iter 38: .........*(NumConst=38, SV=7, CEps=4.2602, QPEps=0.4559)
Iter 39: .........*(NumConst=39, SV=7, CEps=3.7223, QPEps=1.2228)
Iter 40: .........*(NumConst=40, SV=8, CEps=3.0560, QPEps=0.0000)
Iter 41: .........*(NumConst=41, SV=8, CEps=2.1521, QPEps=1.0660)
Iter 42: .........*(NumConst=42, SV=8, CEps=1.7743, QPEps=0.0012)
Iter 43: .........*(NumConst=43, SV=6, CEps=2.1744, QPEps=0.0321)
Iter 44: .........*(NumConst=44, SV=7, CEps=1.4996, QPEps=0.0791)
Iter 45: .........*(NumConst=45, SV=8, CEps=2.0024, QPEps=0.7248)
Iter 46: .........*(NumConst=46, SV=6, CEps=1.2498, QPEps=0.2943)
Iter 47: .........*(NumConst=47, SV=6, CEps=1.5660, QPEps=0.2245)
Iter 48: .........*(NumConst=48, SV=7, CEps=1.3732, QPEps=0.1007)
Iter 49: .........*(NumConst=49, SV=7, CEps=1.1906, QPEps=0.5808)
Iter 50: .........(NumConst=49, SV=7, CEps=0.6779, QPEps=0.5808)
Final epsilon on KKT-Conditions: 0.67790
Upper bound on duality gap: 0.00712
Dual objective value: dval=6.31688
Primal objective value: pval=6.32400
Total number of constraints in final working set: 49 (of 49)
Number of iterations: 50
Number of calls to 'find_most_violated_constraint': 9000
Number of SV: 7 
Norm of weight vector: |w|=0.87063
Value of slack variable (on working set): xi=593.84427
Value of slack variable (global): xi=594.50017
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=7321.24719
Runtime in cpu-seconds: 0.35
Compacting linear model...done
Writing learned model...done