Reading training examples...done
Training set properties: 19 features, 180 rankings, 15549 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.945178
Iter 1: .........*(NumConst=1, SV=1, CEps=945.1778, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=938.3223, QPEps=0.0005)
Iter 3: .........*(NumConst=3, SV=3, CEps=2104.9147, QPEps=0.0018)
Iter 4: .........*(NumConst=4, SV=4, CEps=1676.1125, QPEps=0.0027)
Iter 5: .........*(NumConst=5, SV=4, CEps=1443.8589, QPEps=0.0006)
Iter 6: .........*(NumConst=6, SV=6, CEps=878.6673, QPEps=0.0014)
Iter 7: .........*(NumConst=7, SV=6, CEps=2057.0883, QPEps=4.0810)
Iter 8: .........*(NumConst=8, SV=6, CEps=1447.3980, QPEps=49.5067)
Iter 9: .........*(NumConst=9, SV=8, CEps=459.6844, QPEps=14.7255)
Iter 10: .........*(NumConst=10, SV=8, CEps=380.2828, QPEps=48.0824)
Iter 11: .........*(NumConst=11, SV=7, CEps=332.7561, QPEps=47.1529)
Iter 12: .........*(NumConst=12, SV=8, CEps=199.8223, QPEps=11.2513)
Iter 13: .........*(NumConst=13, SV=7, CEps=248.5434, QPEps=14.1020)
Iter 14: .........*(NumConst=14, SV=9, CEps=192.1481, QPEps=8.4446)
Iter 15: .........*(NumConst=15, SV=9, CEps=146.0430, QPEps=22.8709)
Iter 16: .........*(NumConst=16, SV=9, CEps=155.5380, QPEps=3.4872)
Iter 17: .........*(NumConst=17, SV=9, CEps=166.3280, QPEps=13.4618)
Iter 18: .........*(NumConst=18, SV=10, CEps=201.8564, QPEps=47.6210)
Iter 19: .........*(NumConst=19, SV=9, CEps=107.5143, QPEps=3.6918)
Iter 20: .........*(NumConst=20, SV=9, CEps=144.5746, QPEps=15.7110)
Iter 21: .........*(NumConst=21, SV=9, CEps=138.7378, QPEps=0.0003)
Iter 22: .........*(NumConst=22, SV=9, CEps=104.5530, QPEps=4.0065)
Iter 23: .........*(NumConst=23, SV=10, CEps=61.8068, QPEps=2.2258)
Iter 24: .........*(NumConst=24, SV=9, CEps=74.7644, QPEps=0.0003)
Iter 25: .........*(NumConst=25, SV=9, CEps=50.1548, QPEps=0.0001)
Iter 26: .........*(NumConst=26, SV=9, CEps=55.6589, QPEps=14.8409)
Iter 27: .........*(NumConst=27, SV=8, CEps=48.1432, QPEps=0.0013)
Iter 28: .........*(NumConst=28, SV=8, CEps=35.1582, QPEps=0.0746)
Iter 29: .........*(NumConst=29, SV=11, CEps=29.7591, QPEps=14.4938)
Iter 30: .........*(NumConst=30, SV=10, CEps=31.9516, QPEps=8.0208)
Iter 31: .........*(NumConst=31, SV=9, CEps=26.4769, QPEps=0.0008)
Iter 32: .........*(NumConst=32, SV=9, CEps=23.2143, QPEps=0.0003)
Iter 33: .........*(NumConst=33, SV=9, CEps=25.6692, QPEps=0.0013)
Iter 34: .........*(NumConst=34, SV=10, CEps=20.5081, QPEps=1.5580)
Iter 35: .........*(NumConst=35, SV=11, CEps=31.2509, QPEps=8.2527)
Iter 36: .........*(NumConst=36, SV=11, CEps=13.4564, QPEps=1.9341)
Iter 37: .........*(NumConst=37, SV=10, CEps=30.7971, QPEps=0.0000)
Iter 38: .........*(NumConst=38, SV=10, CEps=17.0469, QPEps=0.0132)
Iter 39: .........*(NumConst=39, SV=11, CEps=16.8683, QPEps=5.2442)
Iter 40: .........*(NumConst=40, SV=9, CEps=11.6613, QPEps=0.0909)
Iter 41: .........*(NumConst=41, SV=9, CEps=10.5702, QPEps=0.0000)
Iter 42: .........*(NumConst=42, SV=9, CEps=8.8858, QPEps=0.0252)
Iter 43: .........*(NumConst=43, SV=9, CEps=9.4800, QPEps=3.8928)
Iter 44: .........*(NumConst=44, SV=10, CEps=8.0571, QPEps=3.8988)
Iter 45: .........*(NumConst=45, SV=10, CEps=8.9236, QPEps=0.1217)
Iter 46: .........*(NumConst=46, SV=10, CEps=7.7269, QPEps=0.0006)
Iter 47: .........*(NumConst=47, SV=9, CEps=4.3653, QPEps=0.0000)
Iter 48: .........*(NumConst=48, SV=8, CEps=6.5437, QPEps=0.0553)
Iter 49: .........*(NumConst=49, SV=8, CEps=6.4461, QPEps=1.1164)
Iter 50: .........*(NumConst=50, SV=9, CEps=4.7064, QPEps=0.0001)
Iter 51: .........*(NumConst=51, SV=8, CEps=4.8237, QPEps=0.0503)
Iter 52: .........*(NumConst=52, SV=10, CEps=3.3334, QPEps=1.3919)
Iter 53: .........*(NumConst=53, SV=10, CEps=5.4817, QPEps=1.6060)
Iter 54: .........*(NumConst=54, SV=9, CEps=2.6416, QPEps=0.0000)
Iter 55: .........*(NumConst=55, SV=9, CEps=4.1463, QPEps=1.2259)
Iter 56: .........*(NumConst=56, SV=9, CEps=3.7186, QPEps=1.2149)
Iter 57: .........*(NumConst=57, SV=11, CEps=2.2343, QPEps=0.7280)
Iter 58: .........*(NumConst=58, SV=8, CEps=2.9904, QPEps=0.0085)
Iter 59: .........*(NumConst=58, SV=8, CEps=4.7290, QPEps=0.0000)
Iter 60: .........*(NumConst=58, SV=8, CEps=2.0800, QPEps=0.8474)
Iter 61: .........*(NumConst=58, SV=8, CEps=1.8415, QPEps=0.0055)
Iter 62: .........*(NumConst=57, SV=9, CEps=1.2201, QPEps=0.1923)
Iter 63: .........*(NumConst=58, SV=9, CEps=1.9151, QPEps=0.0003)
Iter 64: .........*(NumConst=59, SV=9, CEps=1.8376, QPEps=0.0032)
Iter 65: .........(NumConst=59, SV=9, CEps=0.9306, QPEps=0.0032)
Final epsilon on KKT-Conditions: 0.93056
Upper bound on duality gap: 0.06511
Dual objective value: dval=41.13169
Primal objective value: pval=41.19680
Total number of constraints in final working set: 59 (of 64)
Number of iterations: 65
Number of calls to 'find_most_violated_constraint': 11700
Number of SV: 9 
Norm of weight vector: |w|=1.43885
Value of slack variable (on working set): xi=572.81049
Value of slack variable (global): xi=573.73795
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3635.04444
Runtime in cpu-seconds: 0.27
Compacting linear model...done
Writing learned model...done