Python裡面的scipy庫如何計算線性規劃問題呢？

02-01

就比如解決上述問題，在scipy裡面應該調用哪些函數呢？

pulp試試這個模塊。（以下採用python 3.5）

from pulp import *


# 設置對象

f  = LpProblem(lptest, LpMinimize)
# 設置三個變數，並設置變數最小取值

x = LpVariable(x, lowBound = 0)

y = LpVariable(y, lowBound = 0)

z = LpVariable(z, lowBound = 0)
# 載入約束變數

f += 3.05 * x + 4.05 * y + 6.1 * z &>= 7.9
# 求解

GLPK().solve(f)

# 顯示結果 for i in f.variables(): print(i.name + "=" + str(i.varValue))

顯示如下：

GLPSOL: GLPK LP/MIP Solver, v4.55 Parameter(s) specified in the command line: --cpxlp C:Users onyAppDataLocalTemp12100-pulp.lp -o C:Users onyAppDataLocalTemp12100-pulp.sol Reading problem data from C:Users onyAppDataLocalTemp12100-pulp.lp... 1 row, 4 columns, 3 non-zeros 8 lines were read GLPK Simplex Optimizer, v4.55 1 row, 4 columns, 3 non-zeros Preprocessing... 1 row, 3 columns, 3 non-zeros Scaling... A: min|aij| = 3.050e+000 max|aij| = 6.100e+000 ratio = 2.000e+000 Problem data seem to be well scaled Constructing initial basis... Size of triangular part is 1 0: obj = 0.000000000e+000 infeas = 7.900e+000 (0) * 1: obj = 0.000000000e+000 infeas = 0.000e+000 (0) OPTIMAL LP SOLUTION FOUND Time used: 0.0 secs Memory used: 0.0 Mb (36952 bytes) Writing basic solution to C:Users onyAppDataLocalTemp12100-pulp.sol... 1

__dummy=None x=0.0 y=0.0 z=1.29508

scipy里有個linprog模塊做線性規劃，先進行參數設置再做優化。

from scipy.optimize import linprog


c = [1,1,1]

A = [[3.05, 4.05, 6.1]]

b = [[7.9]]
x_bounds = (0, None)

y_bounds = (0, None)

z_bounds = (0, None)

res = linprog(c, A_ub=A, b_ub=b, bounds=(x_bounds, y_bounds, z_bounds), ... options={"disp": True})

優化：

Optimization terminated successfully. Current function value: -0.000000 Iterations: 0

結果：

fun: -0.0 message: Optimization terminated successfully. nit: 0 slack: array([ 7.9]) status: 0 success: True x: array([ 0., 0., 0.])

想問一下有沒有做整數規劃的包在python中，找了半天找不到！

import numpy as np

from scipy.optimize import linprog

a = np.array([1, 1, 1])

b = np.array([-7.9])

c = np.array([[-3.05, -4.05, -6.1]])

res = linprog(a, A_ub=c, b_ub=b)

print(res)

這樣子是有解的

scipy.optimize.linprog()

你這個模型應該是沒有解的。

import numpy as np

import matplotlib.pyplot as mpl

from scipy import optimize

c=np.array([1,1,1])

a=np.array([[3.05,4.0,6.15]])

b=np.array([7.9])

res=optimize.linprog(c,-a,b)

print res.x

答案：

[ 0. 0. 0.]