使用LBFGS求解支持向量机对偶问题，参考了Matlab Software by Mark Schmidt： http://www.di.ens.fr/~mschmidt/Software/minConf.html
LBFGS用于求解大规模的优化问题，已经被广泛的应用在各种机器学习问题中。关于LBFGS的相关内容可以参见下面两个链接：
http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm http://www.codelast.com/?p=2780
main.cpp是一个测试用例，数据集数量为250，选用其中150个作为训练集，其余的作为测试集。
关于这个程序我写了一个详细的文档，不久会发到网上~
编译程序：
javier@javier-Lenovo:~/Workspace/libminconf_svm$ make
Building obj/array.o...
Building obj/minconf_tmp.o...
Building obj/polyinterp.o...
Building obj/svm.o...
Building library...
ar: creating ./lib/libminconf_tmp.a
javier@javier-Lenovo:~/Workspace/libminconf_svm$ make svm
Building train executable file ...
程序运行结果：
javier@javier-Lenovo:~/Workspace/libminconf_svm$ ./bin/svm
Reading data...
Iteration FunEvals Step Length Function Val Opt Cond
L-BFGS
1 2 6.66667e-03 -9.80344e-01 1.44103e+02
2 3 1.00000e+00 -1.73692e+01 1.70724e+02
3 4 1.00000e+00 -2.35502e+01 2.42023e+02
4 5 1.00000e+00 -3.09044e+01 1.42131e+02
5 6 1.00000e+00 -4.45824e+01 5.88137e+01
6 7 1.00000e+00 -4.80298e+01 3.81936e+01
7 8 1.00000e+00 -5.01108e+01 2.25573e+01
8 9 1.00000e+00 -5.17151e+01 1.12310e+01
9 10 1.00000e+00 -5.27262e+01 5.07007e+00
10 11 1.00000e+00 -5.31558e+01 5.87705e+00
11 12 1.00000e+00 -5.33095e+01 3.70394e+00
12 13 1.00000e+00 -5.34772e+01 2.80490e+00
13 14 1.00000e+00 -5.36690e+01 1.13255e+00
14 15 1.00000e+00 -5.37062e+01 1.25654e+00
15 16 1.00000e+00 -5.37273e+01 1.52874e+00
16 17 1.00000e+00 -5.37440e+01 1.04342e+00
17 18 1.00000e+00 -5.37573e+01 9.59199e-01
Cubic Backtracking
18 20 1.73880e-01 -5.37632e+01 7.25757e-01
19 21 1.00000e+00 -5.37832e+01 6.12949e-01
20 22 1.00000e+00 -5.38136e+01 7.61441e-01
21 23 1.00000e+00 -5.38469e+01 1.08702e+00
22 24 1.00000e+00 -5.38568e+01 2.50417e-01
23 25 1.00000e+00 -5.38885e+01 4.04117e-01
Cubic Backtracking
24 27 1.23728e-01 -5.38940e+01 4.98169e-01
Cubic Backtracking
25 29 6.32498e-02 -5.39102e+01 2.00344e-01
Cubic Backtracking
Interpolated value too large, Adjusting
Cubic Backtracking
Interpolated value too large, Adjusting
Cubic Backtracking
Cubic Backtracking
Cubic Backtracking
Cubic Backtracking
26 36 2.65240e-05 -5.39102e+01 2.00395e-01
Cubic Backtracking
27 38 1.14767e-01 -5.39160e+01 1.72371e-01
28 39 1.00000e+00 -5.39226e+01 1.69142e-01
Cubic Backtracking
29 41 5.84268e-02 -5.39252e+01 1.63674e-01
Cubic Backtracking
Cubic Backtracking
30 44 5.24389e-03 -5.39263e+01 1.47500e-01
Cubic Backtracking
31 46 3.22505e-02 -5.39272e+01 1.05750e-01
Cubic Backtracking
Cubic Backtracking
32 49 1.23244e-02 -5.39280e+01 7.43283e-02
Cubic Backtracking
33 51 9.04024e-02 -5.39343e+01 3.80322e-02
Cubic Backtracking
Cubic Backtracking
Cubic Backtracking
34 55 5.70366e-03 -5.39346e+01 2.64456e-02
35 56 1.00000e+00 -5.39352e+01 5.40733e-02
Cubic Backtracking
36 58 3.24925e-01 -5.39369e+01 3.56061e-02
37 59 1.00000e+00 -5.39374e+01 1.15466e-02
38 60 1.00000e+00 -5.39374e+01 9.66379e-03
39 61 1.00000e+00 -5.39374e+01 9.28494e-03
40 62 1.00000e+00 -5.39375e+01 1.64861e-02
41 63 1.00000e+00 -5.39377e+01 2.97212e-02
42 64 1.00000e+00 -5.39382e+01 5.28358e-02
Cubic Backtracking
43 66 2.41418e-01 -5.39385e+01 6.16050e-02
Cubic Backtracking
44 68 8.15000e-03 -5.39385e+01 6.23130e-02
Cubic Backtracking
45 70 1.99812e-02 -5.39388e+01 6.53351e-02
Cubic Backtracking
46 72 3.50423e-02 -5.39392e+01 7.06420e-02
Cubic Backtracking
47 74 1.61775e-01 -5.39401e+01 7.02310e-03
Cubic Backtracking
48 76 4.35884e-01 -5.39403e+01 6.15119e-03
49 77 1.00000e+00 -5.39404e+01 1.05008e-03
50 78 1.00000e+00 -5.39404e+01 2.88398e-17
All working variables satisfy optimality condition.
Train Error:0.06
Generalized Error:0.06