方法一（直接解）
代码
min=4*x11+12*x12+4*x13+11*x14 +2*x21+10*x22+3*x23+9*x24 +8*x31+5*x32+11*x33+6*x34; x11+x12+x13+x14=16; x21+x22+x23+x24=10; x31+x32+x33+x34=22; x11+x21+x31=8; x12+x22+x32=14; x13+x23+x33=12; x14+x24+x34=14;
结果
Global optimal solution found. Objective value: 244.0000 Infeasibilities: 0.000000 Total solver iterations: 7 Variable Value Reduced Cost X11 0.000000 0.000000 X12 0.000000 2.000000 X13 12.00000 0.000000 X14 4.000000 0.000000 X21 8.000000 0.000000 X22 0.000000 2.000000 X23 0.000000 1.000000 X24 2.000000 0.000000 X31 0.000000 9.000000 X32 14.00000 0.000000 X33 0.000000 12.00000 X34 8.000000 0.000000 Row Slack or Surplus Dual Price 1 244.0000 -1.000000 2 0.000000 -4.000000 3 0.000000 -2.000000 4 0.000000 1.000000 5 0.000000 0.000000 6 0.000000 -6.000000 7 0.000000 0.000000 8 0.000000 -7.000000
缺点，数据多时不好找
方法二（化简）
当变量有成千上万个时，而关心的非零解只是极少数，在当前窗口读解很麻烦。下面是读取非零解的窗口操作步骤：
（1）缩小当前解的窗口（不是关闭！）；
（2）把鼠标点进模型所在窗口；
结果
Global optimal solution found. Objective value: 244.0000 Infeasibilities: 0.000000 Total solver iterations: 7 Variable Value Reduced Cost X13 12.00000 0.000000 X14 4.000000 0.000000 X21 8.000000 0.000000 X24 2.000000 0.000000 X32 14.00000 0.000000 X34 8.000000 0.000000 Row Slack or Surplus Dual Price 2 0.000000 -4.000000 3 0.000000 -2.000000 4 0.000000 1.000000 5 0.000000 0.000000 6 0.000000 -6.000000 7 0.000000 0.000000 8 0.000000 -7.000000
