Reading training examples...done
Training set properties: 25 features, 90 rankings, 8124 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.488833
Iter 1: .........*(NumConst=1, SV=1, CEps=488.8333, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=392.3085, QPEps=0.0001)
Iter 3: .........*(NumConst=3, SV=3, CEps=1165.9839, QPEps=0.0001)
Iter 4: .........*(NumConst=4, SV=3, CEps=799.7892, QPEps=21.0298)
Iter 5: .........*(NumConst=5, SV=4, CEps=579.9117, QPEps=0.0016)
Iter 6: .........*(NumConst=6, SV=6, CEps=220.6920, QPEps=22.7518)
Iter 7: .........*(NumConst=7, SV=5, CEps=129.0781, QPEps=7.8349)
Iter 8: .........*(NumConst=8, SV=5, CEps=98.7353, QPEps=12.7776)
Iter 9: .........*(NumConst=9, SV=5, CEps=70.6363, QPEps=31.3650)
Iter 10: .........*(NumConst=10, SV=6, CEps=80.0266, QPEps=31.6002)
Iter 11: .........*(NumConst=11, SV=6, CEps=67.8492, QPEps=18.4979)
Iter 12: .........*(NumConst=12, SV=7, CEps=44.0777, QPEps=21.8504)
Iter 13: .........*(NumConst=13, SV=8, CEps=31.5500, QPEps=8.4308)
Iter 14: .........*(NumConst=14, SV=8, CEps=35.7343, QPEps=15.3311)
Iter 15: .........*(NumConst=15, SV=7, CEps=24.8686, QPEps=10.0701)
Iter 16: .........*(NumConst=16, SV=7, CEps=19.9070, QPEps=9.9274)
Iter 17: .........*(NumConst=17, SV=7, CEps=14.3369, QPEps=6.8503)
Iter 18: .........*(NumConst=18, SV=7, CEps=11.2208, QPEps=5.2741)
Iter 19: .........*(NumConst=19, SV=7, CEps=8.7343, QPEps=4.1894)
Iter 20: .........*(NumConst=20, SV=8, CEps=5.7761, QPEps=2.8212)
Iter 21: .........*(NumConst=21, SV=8, CEps=6.0911, QPEps=2.7222)
Iter 22: .........*(NumConst=22, SV=7, CEps=3.7008, QPEps=1.8217)
Iter 23: .........*(NumConst=23, SV=7, CEps=3.5030, QPEps=1.6914)
Iter 24: .........*(NumConst=24, SV=7, CEps=2.5537, QPEps=1.0368)
Iter 25: .........*(NumConst=25, SV=7, CEps=2.0649, QPEps=0.9390)
Iter 26: .........*(NumConst=26, SV=7, CEps=1.8518, QPEps=0.7649)
Iter 27: .........*(NumConst=27, SV=7, CEps=0.7932, QPEps=0.3428)
Iter 28: .........*(NumConst=28, SV=7, CEps=0.5212, QPEps=0.2168)
Iter 29: .........*(NumConst=29, SV=6, CEps=0.8048, QPEps=0.2090)
Iter 30: .........(NumConst=29, SV=6, CEps=0.4240, QPEps=0.2090)
Final epsilon on KKT-Conditions: 0.42399
Upper bound on duality gap: 0.00953
Dual objective value: dval=9.23677
Primal objective value: pval=9.24630
Total number of constraints in final working set: 29 (of 29)
Number of iterations: 30
Number of calls to 'find_most_violated_constraint': 2700
Number of SV: 6 
Norm of weight vector: |w|=0.78867
Value of slack variable (on working set): xi=297.60415
Value of slack variable (global): xi=297.84328
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=2191.55398
Runtime in cpu-seconds: 0.27
Compacting linear model...done
Writing learned model...done