Reading training examples...done
Training set properties: 19 features, 180 rankings, 15673 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.998928
Iter 1: .........*(NumConst=1, SV=1, CEps=998.9278, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1015.1817, QPEps=0.0004)
Iter 3: .........*(NumConst=3, SV=3, CEps=2374.0068, QPEps=0.0015)
Iter 4: .........*(NumConst=4, SV=4, CEps=1231.8434, QPEps=0.0004)
Iter 5: .........*(NumConst=5, SV=5, CEps=314.7485, QPEps=0.0003)
Iter 6: .........*(NumConst=6, SV=5, CEps=322.5499, QPEps=0.0003)
Iter 7: .........*(NumConst=7, SV=5, CEps=294.4384, QPEps=44.6559)
Iter 8: .........*(NumConst=8, SV=5, CEps=135.4235, QPEps=0.2069)
Iter 9: .........*(NumConst=9, SV=6, CEps=104.8124, QPEps=21.6747)
Iter 10: .........*(NumConst=10, SV=6, CEps=229.4343, QPEps=49.5844)
Iter 11: .........*(NumConst=11, SV=7, CEps=100.6459, QPEps=34.6303)
Iter 12: .........*(NumConst=12, SV=8, CEps=91.2273, QPEps=45.0518)
Iter 13: .........*(NumConst=13, SV=7, CEps=61.4251, QPEps=21.0530)
Iter 14: .........*(NumConst=14, SV=8, CEps=48.2455, QPEps=18.7417)
Iter 15: .........*(NumConst=15, SV=8, CEps=24.6931, QPEps=12.2374)
Iter 16: .........*(NumConst=16, SV=9, CEps=30.2330, QPEps=8.3894)
Iter 17: .........*(NumConst=17, SV=9, CEps=17.4919, QPEps=8.2585)
Iter 18: .........*(NumConst=18, SV=9, CEps=14.2426, QPEps=7.0594)
Iter 19: .........*(NumConst=19, SV=9, CEps=16.1225, QPEps=7.0543)
Iter 20: .........*(NumConst=20, SV=9, CEps=12.9133, QPEps=5.4359)
Iter 21: .........*(NumConst=21, SV=10, CEps=8.4493, QPEps=3.9291)
Iter 22: .........*(NumConst=22, SV=10, CEps=6.8445, QPEps=1.4516)
Iter 23: .........*(NumConst=23, SV=10, CEps=6.5601, QPEps=2.8733)
Iter 24: .........*(NumConst=24, SV=9, CEps=4.5008, QPEps=1.5077)
Iter 25: .........*(NumConst=25, SV=9, CEps=3.0331, QPEps=1.2813)
Iter 26: .........*(NumConst=26, SV=10, CEps=3.4493, QPEps=1.2091)
Iter 27: .........*(NumConst=27, SV=11, CEps=2.3877, QPEps=1.0498)
Iter 28: .........*(NumConst=28, SV=11, CEps=2.4812, QPEps=0.8714)
Iter 29: .........*(NumConst=29, SV=11, CEps=1.4815, QPEps=0.6410)
Iter 30: .........*(NumConst=30, SV=11, CEps=1.2154, QPEps=0.5838)
Iter 31: .........*(NumConst=31, SV=11, CEps=1.7922, QPEps=0.5885)
Iter 32: .........(NumConst=31, SV=11, CEps=0.8495, QPEps=0.5885)
Final epsilon on KKT-Conditions: 0.84954
Upper bound on duality gap: 0.00805
Dual objective value: dval=6.80839
Primal objective value: pval=6.81644
Total number of constraints in final working set: 31 (of 31)
Number of iterations: 32
Number of calls to 'find_most_violated_constraint': 5760
Number of SV: 11 
Norm of weight vector: |w|=0.85118
Value of slack variable (on working set): xi=644.99093
Value of slack variable (global): xi=645.41805
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=3598.38556
Runtime in cpu-seconds: 0.23
Compacting linear model...done
Writing learned model...done