In [7]:
import numpy as np
from scipy import linalg
from cvxpy import *

#Denote for A Matrix
A = np.matrix([[1, 1, 1, 0, 0],
              [-1, 1, 0, 1, 0],
              [1, -1, 0, 0, 1]])

#Denoting b for constraints
b = np.array([3, 2, 0])

#Denoting C for objective function coefficient values
c = np.array([-2, 0, 0, 0, 0])


def compute_basic_solutions(val1, val2, val3):
    val1 -= 1
    val2 -= 1
    val3 -= 1
    A_B = A[:, (val1, val2, val3)]
    c_B = c[(val1, val2, val3),]
    print("A_B = ")
    print(A_B)
    print("c_B = ")
    print(c_B)
    x_B = linalg.inv(A_B).dot(b)
    print("x_B = [x_{0}, x_{1}, x_{2}] = ".format(val1 + 1, val2 + 1, val3 + 1))
    print(x_B)
In [8]:
print(compute_basic_solutions(1,3,4))

A_B = 
[[ 1  1  0]
 [-1  0  1]
 [ 1  0  0]]
c_B = 
[-2  0  0]
x_B = [x_1, x_3, x_4] = 
[0. 3. 2.]
None
In [9]:
print(compute_basic_solutions(1,2,5))

A_B = 
[[ 1  1  0]
 [-1  1  0]
 [ 1 -1  1]]
c_B = 
[-2  0  0]
x_B = [x_1, x_2, x_5] = 
[0.5 2.5 2. ]
None
In [10]:
print(compute_basic_solutions(1,4,5))

A_B = 
[[ 1  0  0]
 [-1  1  0]
 [ 1  0  1]]
c_B = 
[-2  0  0]
x_B = [x_1, x_4, x_5] = 
[ 3.  5. -3.]
None
In [13]:
print(compute_basic_solutions(1,2,4))

A_B = 
[[ 1  1  0]
 [-1  1  1]
 [ 1 -1  0]]
c_B = 
[-2  0  0]
x_B = [x_1, x_2, x_4] = 
[1.5 1.5 2. ]
None
In [14]:
print(compute_basic_solutions(2,3,4))

A_B = 
[[ 1  1  0]
 [ 1  0  1]
 [-1  0  0]]
c_B = 
[0 0 0]
x_B = [x_2, x_3, x_4] = 
[0. 3. 2.]
None
In [16]:
print(compute_basic_solutions(2,4,5))

A_B = 
[[ 1  0  0]
 [ 1  1  0]
 [-1  0  1]]
c_B = 
[0 0 0]
x_B = [x_2, x_4, x_5] = 
[ 3. -1.  3.]
None
In [17]:
print(compute_basic_solutions(3,4,5))

A_B = 
[[1 0 0]
 [0 1 0]
 [0 0 1]]
c_B = 
[0 0 0]
x_B = [x_3, x_4, x_5] = 
[3. 2. 0.]
None
In [ ]: