Reading training examples...done
Training set properties: 49 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=1575.0641, QPEps=0.0003)
Iter 3: .........*(NumConst=3, SV=3, CEps=1541.9073, QPEps=0.0007)
Iter 4: .........*(NumConst=4, SV=4, CEps=1513.8590, QPEps=0.0113)
Iter 5: .........*(NumConst=5, SV=4, CEps=1242.6596, QPEps=0.0027)
Iter 6: .........*(NumConst=6, SV=5, CEps=1037.8570, QPEps=0.0109)
Iter 7: .........*(NumConst=7, SV=5, CEps=1463.8401, QPEps=13.9363)
Iter 8: .........*(NumConst=8, SV=6, CEps=756.8742, QPEps=10.1712)
Iter 9: .........*(NumConst=9, SV=7, CEps=685.8382, QPEps=0.0261)
Iter 10: .........*(NumConst=10, SV=7, CEps=266.5065, QPEps=3.5406)
Iter 11: .........*(NumConst=11, SV=8, CEps=223.8820, QPEps=13.0124)
Iter 12: .........*(NumConst=12, SV=7, CEps=385.4309, QPEps=5.3538)
Iter 13: .........*(NumConst=13, SV=8, CEps=216.4156, QPEps=0.0016)
Iter 14: .........*(NumConst=14, SV=9, CEps=251.0977, QPEps=0.0044)
Iter 15: .........*(NumConst=15, SV=8, CEps=199.8872, QPEps=1.3748)
Iter 16: .........*(NumConst=16, SV=9, CEps=128.2635, QPEps=0.0009)
Iter 17: .........*(NumConst=17, SV=9, CEps=175.4769, QPEps=21.8225)
Iter 18: .........*(NumConst=18, SV=8, CEps=118.1597, QPEps=12.7798)
Iter 19: .........*(NumConst=19, SV=9, CEps=109.0259, QPEps=0.0041)
Iter 20: .........*(NumConst=20, SV=9, CEps=96.1926, QPEps=0.0003)
Iter 21: .........*(NumConst=21, SV=8, CEps=91.3649, QPEps=0.0001)
Iter 22: .........*(NumConst=22, SV=9, CEps=55.9822, QPEps=0.1685)
Iter 23: .........*(NumConst=23, SV=9, CEps=77.2170, QPEps=0.0003)
Iter 24: .........*(NumConst=24, SV=8, CEps=63.3111, QPEps=0.0009)
Iter 25: .........*(NumConst=25, SV=8, CEps=78.4094, QPEps=0.0028)
Iter 26: .........*(NumConst=26, SV=9, CEps=46.6080, QPEps=0.0001)
Iter 27: .........*(NumConst=27, SV=8, CEps=51.0979, QPEps=0.0010)
Iter 28: .........*(NumConst=28, SV=9, CEps=43.4139, QPEps=0.0007)
Iter 29: .........*(NumConst=29, SV=9, CEps=40.4965, QPEps=0.0009)
Iter 30: .........*(NumConst=30, SV=9, CEps=31.0038, QPEps=3.5626)
Iter 31: .........*(NumConst=31, SV=9, CEps=40.8572, QPEps=0.0025)
Iter 32: .........*(NumConst=32, SV=9, CEps=28.3919, QPEps=0.0088)
Iter 33: .........*(NumConst=33, SV=9, CEps=20.6688, QPEps=6.1297)
Iter 34: .........*(NumConst=34, SV=9, CEps=23.8764, QPEps=7.3430)
Iter 35: .........*(NumConst=35, SV=9, CEps=27.9044, QPEps=8.5891)
Iter 36: .........*(NumConst=36, SV=9, CEps=17.7621, QPEps=3.2109)
Iter 37: .........*(NumConst=37, SV=9, CEps=19.7802, QPEps=4.6391)
Iter 38: .........*(NumConst=38, SV=9, CEps=18.3408, QPEps=0.0001)
Iter 39: .........*(NumConst=39, SV=10, CEps=13.4114, QPEps=6.1211)
Iter 40: .........*(NumConst=40, SV=9, CEps=13.1605, QPEps=0.0001)
Iter 41: .........*(NumConst=41, SV=8, CEps=12.1984, QPEps=4.9828)
Iter 42: .........*(NumConst=42, SV=8, CEps=11.4154, QPEps=0.0002)
Iter 43: .........*(NumConst=43, SV=8, CEps=8.7690, QPEps=0.0004)
Iter 44: .........*(NumConst=44, SV=8, CEps=8.7878, QPEps=0.0064)
Iter 45: .........*(NumConst=45, SV=11, CEps=6.5583, QPEps=1.9436)
Iter 46: .........*(NumConst=46, SV=11, CEps=13.0347, QPEps=0.0000)
Iter 47: .........*(NumConst=47, SV=10, CEps=8.4567, QPEps=0.0001)
Iter 48: .........*(NumConst=48, SV=8, CEps=4.9486, QPEps=0.0003)
Iter 49: .........*(NumConst=49, SV=9, CEps=5.8742, QPEps=0.0001)
Iter 50: .........*(NumConst=50, SV=9, CEps=5.0948, QPEps=0.0005)
Iter 51: .........*(NumConst=51, SV=8, CEps=6.2587, QPEps=0.7528)
Iter 52: .........*(NumConst=52, SV=8, CEps=5.3192, QPEps=0.0000)
Iter 53: .........*(NumConst=53, SV=9, CEps=3.4779, QPEps=0.6944)
Iter 54: .........*(NumConst=54, SV=7, CEps=2.7617, QPEps=0.0607)
Iter 55: .........*(NumConst=55, SV=8, CEps=5.0155, QPEps=0.0000)
Iter 56: .........*(NumConst=55, SV=9, CEps=2.5024, QPEps=0.3293)
Iter 57: .........*(NumConst=56, SV=8, CEps=3.8509, QPEps=0.2081)
Iter 58: .........*(NumConst=57, SV=7, CEps=3.0373, QPEps=0.0001)
Iter 59: .........*(NumConst=57, SV=9, CEps=1.6130, QPEps=0.6439)
Iter 60: .........*(NumConst=57, SV=7, CEps=3.4727, QPEps=0.0003)
Iter 61: .........*(NumConst=57, SV=6, CEps=1.5693, QPEps=0.1023)
Iter 62: .........*(NumConst=58, SV=7, CEps=2.0404, QPEps=0.1372)
Iter 63: .........*(NumConst=59, SV=7, CEps=2.8382, QPEps=0.0150)
Iter 64: .........*(NumConst=59, SV=8, CEps=1.2776, QPEps=0.5149)
Iter 65: .........*(NumConst=60, SV=8, CEps=2.6419, QPEps=0.4366)
Iter 66: .........*(NumConst=59, SV=8, CEps=1.6022, QPEps=0.1117)
Iter 67: .........*(NumConst=58, SV=7, CEps=1.4895, QPEps=0.1542)
Iter 68: .........(NumConst=58, SV=7, CEps=0.7976, QPEps=0.1542)
Final epsilon on KKT-Conditions: 0.79755
Upper bound on duality gap: 0.02244
Dual objective value: dval=17.36180
Primal objective value: pval=17.38424
Total number of constraints in final working set: 58 (of 67)
Number of iterations: 68
Number of calls to 'find_most_violated_constraint': 12240
Number of SV: 7 
Norm of weight vector: |w|=1.33142
Value of slack variable (on working set): xi=549.26938
Value of slack variable (global): xi=549.92989
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3426.47650
Runtime in cpu-seconds: 0.42
Compacting linear model...done
Writing learned model...done