Reading training examples...done
Training set properties: 49 features, 90 rankings, 8146 examples
NOTE: Adjusted stopping criterion relative to maximum loss: eps=0.497278
Iter 1: .........*(NumConst=1, SV=1, CEps=497.2778, QPEps=0.0000)
Iter 2: .........*(NumConst=2, SV=2, CEps=1217.6843, QPEps=0.0000)
Iter 3: .........*(NumConst=3, SV=3, CEps=699.7245, QPEps=0.0005)
Iter 4: .........*(NumConst=4, SV=4, CEps=1639.5819, QPEps=0.0015)
Iter 5: .........*(NumConst=5, SV=4, CEps=696.3410, QPEps=0.0026)
Iter 6: .........*(NumConst=6, SV=6, CEps=464.9602, QPEps=0.0034)
Iter 7: .........*(NumConst=7, SV=7, CEps=535.5619, QPEps=0.0011)
Iter 8: .........*(NumConst=8, SV=8, CEps=203.9492, QPEps=0.0005)
Iter 9: .........*(NumConst=9, SV=7, CEps=194.6465, QPEps=48.7272)
Iter 10: .........*(NumConst=10, SV=9, CEps=159.3145, QPEps=8.6367)
Iter 11: .........*(NumConst=11, SV=9, CEps=175.4112, QPEps=2.0966)
Iter 12: .........*(NumConst=12, SV=9, CEps=159.6812, QPEps=49.3002)
Iter 13: .........*(NumConst=13, SV=8, CEps=102.3154, QPEps=1.2827)
Iter 14: .........*(NumConst=14, SV=8, CEps=85.6623, QPEps=2.8523)
Iter 15: .........*(NumConst=15, SV=8, CEps=112.6269, QPEps=3.3112)
Iter 16: .........*(NumConst=16, SV=8, CEps=88.4081, QPEps=0.0002)
Iter 17: .........*(NumConst=17, SV=7, CEps=76.7554, QPEps=35.9279)
Iter 18: .........*(NumConst=18, SV=10, CEps=62.6951, QPEps=30.8460)
Iter 19: .........*(NumConst=19, SV=7, CEps=99.6679, QPEps=7.0206)
Iter 20: .........*(NumConst=20, SV=10, CEps=43.9103, QPEps=13.4133)
Iter 21: .........*(NumConst=21, SV=9, CEps=51.8811, QPEps=1.0227)
Iter 22: .........*(NumConst=22, SV=8, CEps=41.6301, QPEps=1.4884)
Iter 23: .........*(NumConst=23, SV=7, CEps=42.5072, QPEps=9.4869)
Iter 24: .........*(NumConst=24, SV=9, CEps=31.9899, QPEps=3.5387)
Iter 25: .........*(NumConst=25, SV=8, CEps=31.4380, QPEps=0.0027)
Iter 26: .........*(NumConst=26, SV=8, CEps=25.7016, QPEps=0.0004)
Iter 27: .........*(NumConst=27, SV=8, CEps=25.4126, QPEps=0.0007)
Iter 28: .........*(NumConst=28, SV=7, CEps=43.4477, QPEps=11.7152)
Iter 29: .........*(NumConst=29, SV=8, CEps=23.1282, QPEps=5.3999)
Iter 30: .........*(NumConst=30, SV=8, CEps=15.6816, QPEps=0.0002)
Iter 31: .........*(NumConst=31, SV=10, CEps=17.0880, QPEps=3.5511)
Iter 32: .........*(NumConst=32, SV=10, CEps=19.0118, QPEps=0.0004)
Iter 33: .........*(NumConst=33, SV=10, CEps=13.3256, QPEps=1.6537)
Iter 34: .........*(NumConst=34, SV=10, CEps=17.2575, QPEps=0.0001)
Iter 35: .........*(NumConst=35, SV=10, CEps=9.4623, QPEps=2.0513)
Iter 36: .........*(NumConst=36, SV=11, CEps=14.4141, QPEps=3.5410)
Iter 37: .........*(NumConst=37, SV=12, CEps=15.4753, QPEps=4.2559)
Iter 38: .........*(NumConst=38, SV=12, CEps=7.8906, QPEps=1.5528)
Iter 39: .........*(NumConst=39, SV=9, CEps=13.8209, QPEps=0.0003)
Iter 40: .........*(NumConst=40, SV=10, CEps=7.5404, QPEps=0.5243)
Iter 41: .........*(NumConst=41, SV=13, CEps=14.8567, QPEps=2.8365)
Iter 42: .........*(NumConst=42, SV=12, CEps=8.4495, QPEps=0.7898)
Iter 43: .........*(NumConst=43, SV=11, CEps=8.8877, QPEps=2.9324)
Iter 44: .........*(NumConst=44, SV=11, CEps=5.2368, QPEps=1.4772)
Iter 45: .........*(NumConst=45, SV=12, CEps=6.9465, QPEps=0.0024)
Iter 46: .........*(NumConst=46, SV=11, CEps=4.4870, QPEps=0.4522)
Iter 47: .........*(NumConst=47, SV=12, CEps=6.5204, QPEps=0.5058)
Iter 48: .........*(NumConst=48, SV=12, CEps=5.0949, QPEps=0.0000)
Iter 49: .........*(NumConst=49, SV=13, CEps=4.5917, QPEps=0.6223)
Iter 50: .........*(NumConst=50, SV=14, CEps=4.2685, QPEps=0.0000)
Iter 51: .........*(NumConst=51, SV=14, CEps=3.8902, QPEps=1.1972)
Iter 52: .........*(NumConst=52, SV=14, CEps=4.6752, QPEps=0.7068)
Iter 53: .........*(NumConst=53, SV=14, CEps=2.7140, QPEps=0.5400)
Iter 54: .........*(NumConst=54, SV=13, CEps=5.0989, QPEps=0.0001)
Iter 55: .........*(NumConst=55, SV=15, CEps=2.2385, QPEps=1.0583)
Iter 56: .........*(NumConst=56, SV=13, CEps=3.7735, QPEps=0.0000)
Iter 57: .........*(NumConst=57, SV=13, CEps=2.2955, QPEps=0.3133)
Iter 58: .........*(NumConst=58, SV=11, CEps=2.5073, QPEps=0.2552)
Iter 59: .........*(NumConst=59, SV=12, CEps=1.3839, QPEps=0.0000)
Iter 60: .........*(NumConst=59, SV=11, CEps=1.8860, QPEps=0.3831)
Iter 61: .........*(NumConst=59, SV=11, CEps=2.6045, QPEps=0.0011)
Iter 62: .........*(NumConst=58, SV=12, CEps=1.1701, QPEps=0.5623)
Iter 63: .........*(NumConst=58, SV=11, CEps=1.9309, QPEps=0.0003)
Iter 64: .........*(NumConst=58, SV=14, CEps=0.9626, QPEps=0.0000)
Iter 65: .........*(NumConst=58, SV=13, CEps=1.4180, QPEps=0.0000)
Iter 66: .........*(NumConst=59, SV=12, CEps=0.9337, QPEps=0.4428)
Iter 67: .........*(NumConst=60, SV=12, CEps=1.2502, QPEps=0.2723)
Iter 68: .........*(NumConst=58, SV=12, CEps=0.6605, QPEps=0.0888)
Iter 69: .........*(NumConst=59, SV=13, CEps=1.1818, QPEps=0.0000)
Iter 70: .........*(NumConst=58, SV=14, CEps=0.5944, QPEps=0.1799)
Iter 71: .........*(NumConst=58, SV=13, CEps=0.9540, QPEps=0.0000)
Iter 72: .........*(NumConst=57, SV=12, CEps=0.5925, QPEps=0.0000)
Iter 73: .........(NumConst=57, SV=12, CEps=0.4401, QPEps=0.0000)
Final epsilon on KKT-Conditions: 0.44008
Upper bound on duality gap: 0.02200
Dual objective value: dval=14.82768
Primal objective value: pval=14.84969
Total number of constraints in final working set: 57 (of 72)
Number of iterations: 73
Number of calls to 'find_most_violated_constraint': 6570
Number of SV: 12 
Norm of weight vector: |w|=1.47186
Value of slack variable (on working set): xi=274.89004
Value of slack variable (global): xi=275.33013
Norm of longest difference vector: ||Psi(x,y)-Psi(x,ybar)||=1451.22798
Runtime in cpu-seconds: 0.23
Compacting linear model...done
Writing learned model...done