Linear Programming double inequalities

In summary, the conversation is about finding the dual of a linear programming problem of the form -d \leq Ax-b \leq d, with additional constraints x \geq 0 and c \cdot x = min. The dual problem can be written as maximize b \cdot y, subject to A^{T}y \leq 0, with y \in \mathbb{R}^m. The attempt at a solution involved converting the problem to canonical form and applying a transpose, but it was suggested to simply re-write the problem in the form of \min \; c \cdot x s.t. F x \geq f x \geq 0. The conversation also touched on combining two separate matrix
  • #1
zcd
200
0

Homework Statement


Find the dual of
[tex]-d \leq Ax-b \leq d[/tex]
[tex] x \geq 0; c \cdot x = min[/tex]
where A is mxn matrix and [tex] x,d,b \in \mathbb{R}^n[/tex]

Homework Equations


dual of canonical is of the form
maximize [tex]b \cdot y[/tex]
[tex]A^{T}y \leq [/tex]
where [tex] y \in \mathbb{R}^m[/tex]

The Attempt at a Solution


I tried converting it to the canonical LP and then applying transpose to A, but it turned out to be a huge mess; is there a simpler way?
 
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  • #2
zcd said:

Homework Statement


Find the dual of
[tex]-d \leq Ax-b \leq d[/tex]
[tex] x \geq 0; c \cdot x = min[/tex]
where A is mxn matrix and [tex] x,d,b \in \mathbb{R}^n[/tex]

Homework Equations


dual of canonical is of the form
maximize [tex]b \cdot y[/tex]
[tex]A^{T}y \leq [/tex]
where [tex] y \in \mathbb{R}^m[/tex]

The Attempt at a Solution


I tried converting it to the canonical LP and then applying transpose to A, but it turned out to be a huge mess; is there a simpler way?

Do you know how to write the dual of an LP of the form
[tex] \min \; c \cdot x [/tex]
s.t.
[tex]F x \geq f [/tex]
[tex] x \geq 0 ? [/tex]
Just re-write your LP in that form and use what you already know.

RGV
 
  • #3
How would I combine two separate matrix inequalities to one?
[tex]Ax \geq b-d[/tex]
[tex]-Ax \geq -b-d[/tex]
 
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  • #4
zcd said:
How would I combine two separate matrix inequalities to one?
[tex]Ax \geq b-d[/tex]
[tex]-Ax \geq -b-d[/tex]

You've just done it. Can't you see what the matrix F is?

RGV
 
  • #5
My guess would be to make a block matrix on the left with A and -A vertically and a block vector with b-d and -b-d on the right. Could it be this simple?
 
  • #6
zcd said:
My guess would be to make a block matrix on the left with A and -A vertically and a block vector with b-d and -b-d on the right. Could it be this simple?

Yes.

RGV
 

FAQ: Linear Programming double inequalities

1. What is linear programming?

Linear programming is a mathematical method used to optimize a certain objective, subject to a set of constraints. It involves finding the maximum or minimum value of a linear function, while satisfying a set of linear inequalities. Linear programming is commonly used in business and economics to make decisions about resource allocation and production planning.

2. What are double inequalities in linear programming?

In linear programming, double inequalities refer to constraints that involve both an upper and a lower bound for a variable. For example, x ≥ 5 and x ≤ 10 are double inequalities. These constraints limit the possible values of a variable to a specific range.

3. How do you graph double inequalities in linear programming?

To graph double inequalities, you first need to plot the individual inequalities on a coordinate plane. Then, the feasible region - the area that satisfies all the constraints - is the overlapping region of the individual inequalities. The optimal solution to the linear programming problem will be one of the corner points of this feasible region.

4. What are the steps for solving a linear programming problem with double inequalities?

The steps for solving a linear programming problem with double inequalities are:
1. Identify the objective function and the constraints
2. Graph the constraints on a coordinate plane
3. Find the feasible region - the overlapping area of the individual constraints
4. Identify the corner points of the feasible region
5. Substitute the corner points into the objective function to find the maximum or minimum value
6. Check if the solution satisfies all the constraints

5. What are some real-life applications of linear programming with double inequalities?

Linear programming with double inequalities has various real-life applications, such as in production planning, resource allocation, transportation and distribution, investment portfolio optimization, and diet planning. It is also commonly used in business decision making, such as in determining the optimal product mix, pricing strategies, and resource allocation for projects.

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