python scipy linprog simplex example v 1.5

Question

所有 scipy 示例都使用旧版本，我正在寻找有关如何使用新版本的示例。
https://docs.scipy.org/doc/scipy/reference/optimize.linprog-simplex.html
我创建了一个 super 简单的代码，它打印出问题是无限的。任何帮助表示赞赏。

Minimize x - y

with
x >= 2
y <= 3

In standard form
-x - s1 = -2
y + s2 = 3

许多解决方案之一应该是 x = 2, y = 3

c = [1, -1]
A = [[-1, 0, -1, 0],[0, 1, 0, 1]]
b = [-2, 3]
linprog(c, method="simplex", options={'A': A, 'b': b})

result
------------------
     con: array([], dtype=float64)
     fun: -inf
 message: 'The problem is (trivially) unbounded because there are no non-trivial constraints and a) at least one decision variable is unbounded above and its corresponding cost is negative, or b) at least one decision variable is unbounded below and its corresponding cost is positive. '
     nit: 0
   slack: array([], dtype=float64)
  status: 3
 success: False
       x: array([ 0., inf])

Answer 1

scipy.optimize.linprog(c, method='simplex', callback=None, options={'c0': None, 'A': None, 'b': None, 'postsolve_args': None, 'maxiter': 1000, 'tol': 1e-09, 'disp': False, 'bland': False}, x0=None)

Minimize a linear objective function subject to linear equality and non-negativity constraints

这意味着您在使用此界面时无法解决您的问题。阅读文档中的注释。
您应该使用顶级 linprog取而代之的是模块。由于您没有等式和不等式约束，您的 A_ub, b_ub, A_eq and b_eq矩阵是 None你只需要定义 bounds .

c = [1, -1]
x_bounds = [2, None]
y_bounds = [None, 3]
res = linprog(c, bounds=[x_bounds, y_bounds], method='simplex')

res
Out[]: 
     fun: -1.0
 message: 'Optimization terminated successfully.'
     nit: 2
   slack: array([0., 0., 0., 0.])
  status: 0
 success: True
       x: array([ 2., 3.])