# How to solve the following equations using simplex method?

Software Engineer here,

I am trying to find an algorithm to solve the following problem, basically I have 3 equations that you can see bellow, and all values of X, Y, Z, and Xi, Yi, Zi's are known. The only unknowns are C values that I am trying to find.

I understand Simplex Method has to be used there (or if anything else please suggest).

But I am new to simplex method, and really confused about many things, like for example what is my objective function? I understand all equalities should be changed to 2 inequalities, so that way I have 6 equations, and this can be considered my restrictions? in that case still confused about my objective function. What should I maximize or minimize if I am just trying to find a value?

If anyone can help me understand this better so I can eventually understand how to make a Tableu and solve this using a programming language, will be great. (Links to a good reads are appreciated as well, so far tried wikipedia, wasn't a good help)

am I even on the right path?

Anyway, here are the equations:

Edit: Forgot to add, all variables are between 0 and 1, which is a major constraint I guess.

Edit 2: I made some progress since yesterday, and tried to implement the simplex the way I see. (See the Tableau)(I tried to maximize for the SUM of C's as a goal)

And it kind of works! As in, it did calculate most of the cases exactly right.

Here is how I test if it was correct - I take my numbers feed to simplex, get C's, then I multiply vector of C's with the matrix back again, expecting to get the same X Y Z values I started with. If it's the same, then it worked.

Problem is, there are weird edge cases! That I can't seem to be able to wrap my brain around.

For example this values work perfectly: X = 0.06837372481822968 Y = 0.13674744963645935 Z = 0.022791240364313126

But, this values (literally almost the same) X = 0.06716471165418625 Y = 0.1343294233083725 Z = 0.022388236597180367

fail!, And fail means the resulting C values from simplex are HUGELY different (missing mid part, middle of C's are zeroes), and this when multiplied back with matrix produces different results from initial values.

How can that be? does it mean that simplex fails due to some wrong constraints or? How do I look at this?

To explain this better, take a look how resulting answer of simplex, just collapses with this little number chance (I checked and during process at some point just different pivot is chosen) Check how third line just dropped in the middle, compared to other two.

This pic kind of suggests that issue is because solutions dip under 0, for whatever reason? not sure why and how to prevent that.

• I don't think this problem is well posed; i.e., without an objective function for us to minimize the class of solutions $C_1, \dots, C_{10}$ is large. – David Kraemer Feb 18 '19 at 3:14
• @DavidKraemer maybe some kind of pseudo objective function can be put in place, like (maximize) C1+C2..+C10 = P ? – Avetis Zakharyan Feb 18 '19 at 4:23
• Please do not use pictures for critical portions of your post. Pictures may not be legible, cannot be searched and are not view-able to some, such as those who use screen readers. For a technical solution, you make search "linear programming octave", and you'll find some of my Octave examples, which can be run on Octave Online. You may see, for example, my "simplex tableau" answers for the theoretical stuff. – GNUSupporter 8964民主女神 地下教會 Feb 21 '19 at 18:16
• The simplex method is an algorithm for an optimization model. You should phrase that model before you resort to algorithms. – LinAlg Feb 22 '19 at 14:43

Linear programming is about optimizing a linear objective function over a polyhedral. Ask yourself, what are the desirable property that you intend to optimize. If your goal is just to obtain a feasible point, you can just optimize $$0$$, the trivial objective function. Or you could have optimize the sum of the variable (which is what you have chosen).

Now, about your weird cases. The most alarming information to me is

and this when multiplied back with matrix produces different results from initial values.

If I understand this correctly and you mean $$Ax =b$$ no longer hold approximately, then there is some bug.

Simplex algorithm should preserve the equality constraint at all time theoretically. In practice, there could be some minor numerical differences of course. If this is not the case, then something has gone wrong. You might want to capture the first moment this condition is violated and also check your checking procedure.

I have called the simplex function from Python Scipy library to check if the reported behavior can be reproduced. Do check if I have constructed your constraint matrix correctly.

import numpy as np
from scipy import optimize
sub_A = np.array([[0.003, 0.168, 0.098, 0.122, 0.502, 0.454, 0.072, 0.003, 0, 0],[0, 0.028, 0.169, 0.503, 0.539, 0.231, 0.029, 0.001, 0 , 0], [0.015, 0.854, 0.698, 0.042, 0, 0,0,0,0,0]])
top = np.concatenate((sub_A, np.zeros((3,10))), axis = 1 )
bottom = np.concatenate((np.identity(10), np.identity(10)), axis =1)
fixed_A = np.concatenate((top, bottom), axis = 0)
fixed_C = np.concatenate((-np.ones(10), np.zeros(10)), axis = 0)

def linprogcheck(x, y, z):
b = np.concatenate((np.array([x,y,z]), np.ones(10)), axis = 0)
ans = optimize.linprog(fixed_C, method = 'simplex', A_eq = fixed_A, b_eq = b)
print("check error size:")
print(np.linalg.norm(np.matmul(fixed_A, ans.x)-b))
return ans

ans1 = linprogcheck(0.06837372481822968 ,0.13674744963645935 , 0.022791240364313126 )
print(ans1)

ans2 = linprogcheck(0.06716471165418625, 0.1343294233083725, 0.022388236597180367)
print(ans2)


The output that I obtained shows that the equality constraint holds and the two solutions are closed to each other.

check error size:
1.4304896245381992e-17
fun: -4.542226973252382
message: 'Optimization terminated successfully.'
nit: 20
slack: array([], dtype=float64)
status: 0
success: True
x: array([0.83814691, 0.        , 0.        , 0.2433104 , 0.        ,
0.        , 0.46076966, 1.        , 1.        , 1.        ,
0.16185309, 1.        , 1.        , 0.7566896 , 1.        ,
1.        , 0.53923034, 0.        , 0.        , 0.        ])
check error size:
3.122502256758253e-17
fun: -4.514192719488299
message: 'Optimization terminated successfully.'
nit: 20
slack: array([], dtype=float64)
status: 0
success: True
x: array([0.82330393, 0.        , 0.        , 0.23901614, 0.        ,
0.        , 0.45187266, 1.        , 1.        , 1.        ,
0.17669607, 1.        , 1.        , 0.76098386, 1.        ,
1.        , 0.54812734, 0.        , 0.        , 0.        ])

• A simple case that I can have failing, goes like this: C1 + 2C2 + 3C3 = 14 | 4C1 + 5C2 + 6C3 = 32 | 7C1 + 8C2 + 9C3 = 50 | Obvious answers for C1, C2, C3 are - 1, 2 and 3. But with this approach to simlpex this numbers are not found. I tried to do this having constraints of C1, C2, C3 <= 4. (I mean I tried this by hand in excel sheet, without a program. so there is something I am doing wrong) – Avetis Zakharyan Feb 24 '19 at 17:26
• you have illustrated that a solution is $1,2,3$, that is the linear programming problem is feasible. simplex algorithm can give you an alternative solution right? Notice that $(1,2,3)$ is not a vertex for your linear program. Simplex algorithm finds vertices. Yes, something could be wrong with your approaches, focus on the first place when equality doesn't hold. – Siong Thye Goh Feb 24 '19 at 17:37
• I don't thin there is alternative solution though, 1, 2, 3, are probably the only solution. And when I do it in excel with simplex. I have to add additional variables. here is my tableu for that, it finishes in 3 steps, and in final steps solutions are .. wrong. (imgur.com/a/qpQ8qc7) So either my initial tableu is incorrect, or I get the method wrong. But result is not 1, 2, 3 – Avetis Zakharyan Feb 26 '19 at 11:12
• @AvetisZakharyan should the $C_i$ be integers? – LinAlg Feb 26 '19 at 18:38
• What is the initial bfs that you use at the beginning?Note that the third constraint is a linear combination of the first two and it can be removed resulting in 5 equations and 6 variables. A bfs (including the slack variable part) has at least one zero. (1,2,3,3,2,1) is not a bfs since it has no zero component, simplex method won't give u that solution. If there is restriction that the solutions are integer, check out cutting plane or branch and bound method of which it uses simplex but simplex method alone doesn't solve an integer programming problem. Remark, I'm traveling, slow response – Siong Thye Goh Feb 27 '19 at 2:16

The main reason for the jumps between unknowns $$C_i$$ in the simplex method is that both the goal function and the constraints of task are symmetric over the unknowns $$C_i$$.

Taking into account that the basic constraints can be presented as the equations of hyperplanes via axes intersections, the upper bounds of the coordinates can be calculated by the formula $$C_i^{max}= \min\left(1, \dfrac X{X_i}, \dfrac Y{Y_i}, \dfrac Z{Z_i}\right).$$ Also, can be used the goal function in the form of $$G = \sum_{i=1}^{10} (1+\varepsilon i) C_i.$$

This should improve the simplex method, both in terms of speed and in terms of sustainability.