Reading training examples...done
Training set properties: 23 features, 180 rankings, 15544 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=1.014872
Iter 1: .........*(NumConst=1, SV=1, CEps=1014.8722, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1059.5022, QPEps=0.0010)
Iter 3: .........*(NumConst=3, SV=3, CEps=2404.1318, QPEps=0.0027)
Iter 4: .........*(NumConst=4, SV=4, CEps=6757.1528, QPEps=0.0033)
Iter 5: .........*(NumConst=5, SV=5, CEps=1248.2137, QPEps=0.0004)
Iter 6: .........*(NumConst=6, SV=5, CEps=1503.1080, QPEps=0.0013)
Iter 7: .........*(NumConst=7, SV=5, CEps=1267.3259, QPEps=49.5957)
Iter 8: .........*(NumConst=8, SV=5, CEps=4233.2247, QPEps=49.8026)
Iter 9: .........*(NumConst=9, SV=6, CEps=5946.9379, QPEps=49.6943)
Iter 10: .........*(NumConst=10, SV=7, CEps=2667.3611, QPEps=49.5449)
Iter 11: .........*(NumConst=11, SV=7, CEps=183.8622, QPEps=49.5015)
Iter 12: .........*(NumConst=12, SV=7, CEps=328.7287, QPEps=49.3517)
Iter 13: .........*(NumConst=13, SV=7, CEps=400.6057, QPEps=46.8598)
Iter 14: .........*(NumConst=14, SV=7, CEps=239.1118, QPEps=45.7225)
Iter 15: .........*(NumConst=15, SV=10, CEps=173.3061, QPEps=47.9804)
Iter 16: .........*(NumConst=16, SV=9, CEps=219.9465, QPEps=49.0960)
Iter 17: .........*(NumConst=17, SV=10, CEps=92.1974, QPEps=46.0592)
Iter 18: .........*(NumConst=18, SV=10, CEps=151.4341, QPEps=31.9034)
Iter 19: .........*(NumConst=19, SV=9, CEps=101.5697, QPEps=37.1317)
Iter 20: .........*(NumConst=20, SV=10, CEps=111.4449, QPEps=25.8947)
Iter 21: .........*(NumConst=21, SV=9, CEps=71.3355, QPEps=32.0625)
Iter 22: .........*(NumConst=22, SV=9, CEps=78.3445, QPEps=32.5363)
Iter 23: .........*(NumConst=23, SV=10, CEps=43.4305, QPEps=21.0109)
Iter 24: .........*(NumConst=24, SV=11, CEps=48.2967, QPEps=21.6142)
Iter 25: .........*(NumConst=25, SV=11, CEps=41.7996, QPEps=19.8880)
Iter 26: .........*(NumConst=26, SV=11, CEps=52.3428, QPEps=20.2814)
Iter 27: .........*(NumConst=27, SV=11, CEps=27.4391, QPEps=13.6702)
Iter 28: .........*(NumConst=28, SV=13, CEps=19.8613, QPEps=9.0421)
Iter 29: .........*(NumConst=29, SV=11, CEps=16.4667, QPEps=8.2155)
Iter 30: .........*(NumConst=30, SV=11, CEps=22.6371, QPEps=8.2251)
Iter 31: .........*(NumConst=31, SV=13, CEps=12.6937, QPEps=6.0550)
Iter 32: .........*(NumConst=32, SV=13, CEps=17.1078, QPEps=6.2246)
Iter 33: .........*(NumConst=33, SV=13, CEps=10.4443, QPEps=5.1557)
Iter 34: .........*(NumConst=34, SV=15, CEps=9.5812, QPEps=4.7121)
Iter 35: .........*(NumConst=35, SV=15, CEps=12.5501, QPEps=4.5020)
Iter 36: .........*(NumConst=36, SV=14, CEps=7.2556, QPEps=3.5497)
Iter 37: .........*(NumConst=37, SV=15, CEps=6.7295, QPEps=3.0866)
Iter 38: .........*(NumConst=38, SV=13, CEps=3.7818, QPEps=1.8823)
Iter 39: .........*(NumConst=39, SV=14, CEps=4.7279, QPEps=1.8472)
Iter 40: .........*(NumConst=40, SV=14, CEps=3.2533, QPEps=1.5178)
Iter 41: .........*(NumConst=41, SV=15, CEps=3.2013, QPEps=1.5127)
Iter 42: .........*(NumConst=42, SV=16, CEps=2.6869, QPEps=1.3121)
Iter 43: .........*(NumConst=43, SV=16, CEps=1.8932, QPEps=0.9152)
Iter 44: .........*(NumConst=44, SV=17, CEps=2.5782, QPEps=0.9121)
Iter 45: .........*(NumConst=45, SV=16, CEps=2.2323, QPEps=0.8559)
Iter 46: .........*(NumConst=46, SV=16, CEps=1.5179, QPEps=0.7183)
Iter 47: .........*(NumConst=47, SV=16, CEps=1.6570, QPEps=0.7563)
Iter 48: .........*(NumConst=48, SV=16, CEps=1.1646, QPEps=0.5100)
Iter 49: .........*(NumConst=49, SV=17, CEps=1.1854, QPEps=0.4988)
Iter 50: .........(NumConst=49, SV=17, CEps=0.9315, QPEps=0.4988)
Final epsilon on KKT-Conditions: 0.93153
Upper bound on duality gap: 0.64575
Dual objective value: dval=632.92691
Primal objective value: pval=633.57266
Total number of constraints in final working set: 49 (of 49)
Number of iterations: 50
Number of calls to 'find_most_violated_constraint': 9000
Number of SV: 17 
Norm of weight vector: |w|=1.67088
Value of slack variable (on working set): xi=631.74365
Value of slack variable (global): xi=632.17673
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=5443.72099
Runtime in cpu-seconds: 41.35
Compacting linear model...done
Writing learned model...done