Reading training examples...done
Training set properties: 19 features, 90 rankings, 8163 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.527100
Iter 1: .........*(NumConst=1, SV=1, CEps=527.1000, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=438.6438, QPEps=0.0002)
Iter 3: .........*(NumConst=3, SV=3, CEps=784.9627, QPEps=0.0004)
Iter 4: .........*(NumConst=4, SV=3, CEps=314.3542, QPEps=0.0001)
Iter 5: .........*(NumConst=5, SV=5, CEps=196.7521, QPEps=0.0016)
Iter 6: .........*(NumConst=6, SV=6, CEps=418.7595, QPEps=0.0006)
Iter 7: .........*(NumConst=7, SV=6, CEps=127.8169, QPEps=37.6374)
Iter 8: .........*(NumConst=8, SV=6, CEps=108.7748, QPEps=0.0239)
Iter 9: .........*(NumConst=9, SV=7, CEps=82.1329, QPEps=38.9046)
Iter 10: .........*(NumConst=10, SV=8, CEps=60.1292, QPEps=29.9390)
Iter 11: .........*(NumConst=11, SV=8, CEps=60.4741, QPEps=29.5048)
Iter 12: .........*(NumConst=12, SV=7, CEps=44.9113, QPEps=22.0095)
Iter 13: .........*(NumConst=13, SV=7, CEps=45.0284, QPEps=11.6249)
Iter 14: .........*(NumConst=14, SV=7, CEps=34.7811, QPEps=15.7528)
Iter 15: .........*(NumConst=15, SV=7, CEps=23.2154, QPEps=11.0101)
Iter 16: .........*(NumConst=16, SV=7, CEps=35.1323, QPEps=9.9231)
Iter 17: .........*(NumConst=17, SV=8, CEps=15.5345, QPEps=2.5646)
Iter 18: .........*(NumConst=18, SV=8, CEps=13.0219, QPEps=6.3628)
Iter 19: .........*(NumConst=19, SV=8, CEps=10.2060, QPEps=3.4055)
Iter 20: .........*(NumConst=20, SV=7, CEps=19.6510, QPEps=4.8367)
Iter 21: .........*(NumConst=21, SV=8, CEps=12.7983, QPEps=4.8303)
Iter 22: .........*(NumConst=22, SV=8, CEps=7.5442, QPEps=3.4956)
Iter 23: .........*(NumConst=23, SV=8, CEps=6.2865, QPEps=1.7035)
Iter 24: .........*(NumConst=24, SV=9, CEps=4.0423, QPEps=1.6484)
Iter 25: .........*(NumConst=25, SV=10, CEps=4.0655, QPEps=1.9546)
Iter 26: .........*(NumConst=26, SV=10, CEps=2.4968, QPEps=1.0442)
Iter 27: .........*(NumConst=27, SV=10, CEps=2.2087, QPEps=1.1002)
Iter 28: .........*(NumConst=28, SV=12, CEps=1.6239, QPEps=0.7934)
Iter 29: .........*(NumConst=29, SV=11, CEps=1.4041, QPEps=0.6140)
Iter 30: .........*(NumConst=30, SV=10, CEps=1.2382, QPEps=0.5841)
Iter 31: .........*(NumConst=31, SV=11, CEps=1.0163, QPEps=0.4913)
Iter 32: .........*(NumConst=32, SV=11, CEps=0.9234, QPEps=0.1817)
Iter 33: .........*(NumConst=33, SV=10, CEps=0.5422, QPEps=0.0791)
Iter 34: .........(NumConst=33, SV=10, CEps=0.4202, QPEps=0.0791)
Final epsilon on KKT-Conditions: 0.42022
Upper bound on duality gap: 0.01183
Dual objective value: dval=9.16547
Primal objective value: pval=9.17730
Total number of constraints in final working set: 33 (of 33)
Number of iterations: 34
Number of calls to 'find_most_violated_constraint': 3060
Number of SV: 10 
Norm of weight vector: |w|=1.17790
Value of slack variable (on working set): xi=282.42136
Value of slack variable (global): xi=282.78590
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=2761.14081
Runtime in cpu-seconds: 0.29
Compacting linear model...done
Writing learned model...done