x8<=x5; x9<=x1; x9<=x2;
@bin(x1);@bin(x2); @bin(x3); @bin(x4); @bin(x5); @bin(x6); @bin(x7);
@bin(x8);@bin(x9);
Global optimal solution found at iteration: 0 Objective value: 6.000000
Variable Value Reduced Cost X1 1.000000 1.000000 X2 1.000000 1.000000 X3 1.000000 1.000000 X4 0.000000 1.000000 X5 0.000000 1.000000 X6 1.000000 1.000000 X7 1.000000 1.000000 X8 0.000000 1.000000 X9 1.000000 1.000000
Row Slack or Surplus Dual Price 1 6.000000 -1.000000 2 1.000000 0.000000 3 0.000000 0.000000 4 1.000000 0.000000 5 0.000000 0.000000 6 0.000000 0.000000 7 1.000000 0.000000 8 1.000000 0.000000 9 1.000000 0.000000 10 0.000000 0.000000 11 0.000000 0.000000 12 0.000000 0.000000 13 0.000000 0.000000
所以最少选修微积分，线性代数，最优化方法，计算机模拟，计算机编程，数学实验
（2）
max=5*x1+4*x2+4*x3+3*x4+4*x5+3*x6+2*x7+2*x8+3*x9;
x1+x2+x3+x4+x5+x6+x7+x8+x9=6; x1+x2+x3+x4+x5>=2; x3+x5+x6+x8+x9>=3; x4+x6+x7+x9>=2; x3<=x1; x3<=x2; x4<=x7; x5<=x1; x5<=x2; x6<=x7; x8<=x5; x9<=x1; x9<=x2;
@bin(x1);@bin(x2); @bin(x3); @bin(x4); @bin(x5); @bin(x6); @bin(x7);
@bin(x8);@bin(x9);
Global optimal solution found at iteration: 6 Objective value: 22.00000
Variable Value Reduced Cost X1 1.000000 -5.000000 X2 1.000000 -4.000000 X3 1.000000 -4.000000 X4 0.000000 -3.000000 X5 1.000000 -4.000000 X6 0.000000 -3.000000 X7 1.000000 -2.000000 X8 0.000000 -2.000000 X9 1.000000 -3.000000
Row Slack or Surplus Dual Price 1 22.00000 1.000000 2 0.000000 0.000000 3 2.000000 0.000000 4 0.000000 0.000000 5 0.000000 0.000000 6 0.000000 0.000000 7 0.000000 0.000000 8 1.000000 0.000000
9 0.000000 0.000000 10 0.000000 0.000000 11 1.000000 0.000000 12 1.000000 0.000000 13 0.000000 0.000000 14 0.000000 0.000000
所以我们应该选x1 x2 x3 x5 x7 x9 即微积分，线性代数，最优化方法 应用统计 计算机编程 数学实验
