Optimal Solution for Linear Programming Problem

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In summary, the tableau in the bottom row has the equation z=1-x_1-2x_3-x_5 which is the optimal solution to the original linear programming problem.
  • #1
evinda
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Hello! (Wave)
I want to find the optimal solution using the simplex method of the following linear programming problem:

$$ \max (-x_1+2x_2-3x_3) \\ x_1-x_2+x_3+2x_4=10 \\ 2x_2-x_3 \leq 1 \\ x_2+ 2x_4 \leq 8 \\ x_i \geq 0, i=1,2,3,4$$

$A=\begin{bmatrix}
1 & -1 & 1 & 2 & 0 & 0\\
0 & 2 & -1 & 0 & 1 & 0\\
0 & 1 & 0 & 2 & 0 & 1
\end{bmatrix}$

A basic feasible non degenerate solution is $(10,0,0,0,1,8)$.

$\begin{bmatrix}
B & c_B & b & P_1 & P_2 & P_3 & P_4 & P_5 & P_6 & \theta & \\
P_1 & -1 & 10 & 1 & -1 & 1 & 2 & 0 & 0 & - & L_1\\
P_5 & 0 & 1 & 0 & 2 & -1 & 0 & 1 & 0 & \frac{1}{2} & L_2\\
P_6 & 0 & 8 & 0 & 1 & 0 & 2 & 0 & 1 & \frac{8}{1} & L_3 \\
& z & 0 & 1 & -2 & 3 & 0 & 0 & 0 & & L_4
\end{bmatrix}$Since $z_2-c_2<0$, $P_2$ will get in the basis and $P_5$ gets out of the basis.

So we get the following tableau:$\begin{bmatrix}
B & c_B & b & P_1 & P_2 & P_3 & P_4 & P_5 & P_6 & \theta & \\
P_1 & -1 & \frac{21}{2} & 1 & 0 & \frac{1}{2} & 2 & \frac{1}{2} & 0 & & L_1'=L_1+L_2'\\
P_2 & 2 & \frac{1}{2} & 0 & 1 & -\frac{1}{2} & 0 & \frac{1}{2} & 0 & & L_2'=\frac{L_2}{2}\\
P_6 & 0 & \frac{15}{2} & 0 & 0 & \frac{1}{2} & 2 & -\frac{1}{2} & 1 & & L_3'=L_3-L_2' \\
& z & 1 & 1 & 0 & 2 & 0 & 1 & 0 & & L_4'=L_4+2L_2'
\end{bmatrix}$

Thus , $-\frac{19}{2}$ is the optimal solution and is achieved at the vertex $\left( \frac{21}{2}, \frac{1}{2}, 0,0, ,0, \frac{15}{2}\right)$.

Is it right or have I done something wrong? (Thinking)
 
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  • #2
Hey evinda! (Smile)

It looks fine to me. (Mmm)
 
  • #3
I like Serena said:
Hey evinda! (Smile)

It looks fine to me. (Mmm)

Nice! What does the bottom row mean in this case and specifically the 1 next to z? (Thinking)
 
  • #4
evinda said:
Nice! What does the bottom row mean in this case and specifically the 1 next to z? (Thinking)

The original objective function is:
$$z=-x_1+2x_3-3x_3= -\frac {21} 2 + 2\cdot \frac 12 = -\frac {19}2$$
It is inserted into the original tableau as:
$$z+x_1-2x_2+3x_3 = 0$$
After the transformations it is:
$$z+x_1+2x_3+x_5=1 \Rightarrow z = 1 - x_1 -2x_3-x_5 = 1 - \frac {21}2 = -\frac{19}2$$
The $1$ next to the $z$ corresponds with the right hand side, just like it does for the other equations. (Mmm)
 
  • #5
I like Serena said:
The original objective function is:
$$z=-x_1+2x_3-3x_3= -\frac {21} 2 + 2\cdot \frac 12 = -\frac {19}2$$
It is inserted into the original tableau as:
$$z+x_1-2x_2+3x_3 = 0$$
After the transformations it is:
$$z+x_1+2x_3+x_5=1 \Rightarrow z = 1 - x_1 -2x_3-x_5 = 1 - \frac {21}2 = -\frac{19}2$$
The $1$ next to the $z$ corresponds with the right hand side, just like it does for the other equations. (Mmm)

I see... Thanks a lot! (Nod)
 

FAQ: Optimal Solution for Linear Programming Problem

What is a linear programming problem?

A linear programming problem is a mathematical optimization technique that is used to find the best solution to a problem with linear relationships between variables. It involves maximizing or minimizing a linear objective function, subject to a set of linear constraints.

What is an optimal solution for a linear programming problem?

An optimal solution for a linear programming problem is the best possible solution that satisfies all of the constraints and achieves the maximum or minimum value of the objective function. It represents the most efficient use of resources and provides the highest possible benefit or profit.

How is an optimal solution for a linear programming problem determined?

The optimal solution for a linear programming problem is determined by using a mathematical algorithm, such as the simplex method or the interior point method, to systematically evaluate all possible solutions and identify the one that meets all constraints and optimizes the objective function.

What are the advantages of using linear programming for problem-solving?

Linear programming offers several advantages for problem-solving, including the ability to model complex systems with many variables and constraints, the ability to find the best solution quickly and efficiently, and the ability to handle problems with uncertain or changing parameters.

What are some common applications of linear programming?

Linear programming has a wide range of applications in various industries, such as finance, transportation, manufacturing, and telecommunications. Some common examples include resource allocation, production planning, portfolio optimization, and network routing.

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