What is the Canonical Form of Linear Programming?

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Discussion Overview

The discussion revolves around the canonical form of linear programming problems, exploring definitions, conditions, and implications related to the structure of such problems. Participants examine the role of the matrix \( A \), the rank condition, and the nature of solutions to the equations defined by \( Ax = b \). The conversation includes both theoretical aspects and specific examples.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • Some participants propose that a linear programming problem is in canonical form if it maximizes a linear objective function subject to equality constraints and non-negativity conditions.
  • There is a question regarding the definition of \( F \) and whether it refers to the set of feasible solutions or a field, with some arguing it is a field, typically \( \mathbb{R} \).
  • Participants discuss the implications of the condition \( r(A) = m < n \), with some suggesting that it indicates the equations are linearly independent and that there are infinitely many solutions to \( Ax = b \).
  • One participant provides an example with \( m = 1 \) and \( n = 2 \), questioning how this leads to infinitely many solutions.
  • Another participant explains that having more variables than independent equations allows for multiple solutions, illustrating this with the concept of setting variables to zero and shifting them around.

Areas of Agreement / Disagreement

Participants express differing views on the definition of \( F \) and the implications of the rank condition. While there is some agreement on the nature of solutions when \( r(A) = m < n \), the discussion remains unresolved regarding the precise definitions and implications of these concepts.

Contextual Notes

There are limitations in the discussion regarding the assumptions made about the definitions of terms like \( F \) and the implications of the rank condition, which are not fully clarified or agreed upon by all participants.

evinda
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Hello! (Wave)

A linear programming problem is in canonical form if it's of the following form:

$$\pm \max (c_1 x_1+ \dots + c_n x_n) , c_1, \dots, c_n \in \mathbb{R} \\ Ax=b, A \in F^{m \times n}, x=\begin{bmatrix}
x_1\\
\dots\\
\dots \\
x_n
\end{bmatrix}, b=\begin{bmatrix}
b_1\\
\dots\\
\dots \\
b_m
\end{bmatrix}$$

$(A=[a_{ij}], a_{ij} \in \mathbb{R})$

$b_j \geq 0, j=1, \dots, m \\ x_i \geq 0, i=1, \dots, n$

$r(A)=m<n$

where $r(A)$ is the rank of the matrix $A$.

$r(A):=$ maximum number of linearly independent rows of the matrix $A$.First of all, could you explain me why it has to hold that $A \in F^{m \times n}$ where $F$ is the set of feasible solutions?
Doesn't $F$ contain only the $x$ s for which it holds that $Ax=b$ ? Or am I wrong?

Also, does the condition $r(A)=m<n$ imply that all the $m$ equations that we get from $Ax=b$ are not multiple the one of the other? Or have I understood it wrong? (Thinking)
 
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evinda said:
First of all, could you explain me why it has to hold that $A \in F^{m \times n}$ where $F$ is the set of feasible solutions?
Doesn't $F$ contain only the $x$ s for which it holds that $Ax=b$ ? Or am I wrong?

Hey evinda! (Smile)

It seems to me that the definitions are a bit mixed up. :eek:
I think $F$ is just a field, typically $\mathbb R$.
The set of feasible solutions is the set of all $x$ that satisfy $Ax=b, x_i \geq 0$, which is a subset of $F^n$.
And the matrix $A$ is an $m\times n$ matrix with elements of $F$, so $A \in F^{m\times n}$. (Mmm)
Also, does the condition $r(A)=m<n$ imply that all the $m$ equations that we get from $Ax=b$ are not multiple the one of the other? Or have I understood it wrong? (Thinking)

Even stronger, anyone of the equations cannot be a linear combinations of any of the other equations.
The equations have to be linearly independent.
And there have to be infinitely many solutions $x$ for $Ax=b$ (Nerd)
 
I like Serena said:
Hey evinda! (Smile)

It seems to me that the definitions are a bit mixed up. :eek:
I think $F$ is just a field, typically $\mathbb R$.
The set of feasible solutions is the set of all $x$ that satisfy $Ax=b, x_i \geq 0$, which is a subset of $F^n$.
And the matrix $A$ is an $m\times n$ matrix with elements of $F$, so $A \in F^{m\times n}$. (Mmm)

Ah, I see... (Nod)

I like Serena said:
Even stronger, anyone of the equations cannot be a linear combinations of any of the other equations.

Any of the $m$ equations, right? (Thinking)

I like Serena said:
And there have to be infinitely many solutions $x$ for $Ax=b$ (Nerd)

Why does this hold?
 
evinda said:
Any of the $m$ equations, right? (Thinking)

Exactly. (Nod)

Why does this hold?

Because $\operatorname{rank} A = m < n$.
That means more columns than rows, while the rows are independent.
In turn that means more variables than equations, which guarantees infinitely many solutions.

As an example, let's pick $F=\mathbb R, m=1, n=2$ and $2x+3y = 6$.
How many solutions does it have? (Wondering)
 
I like Serena said:
As an example, let's pick $F=\mathbb R, m=1, n=2$ and $2x+3y = 6$.
How many solutions does it have? (Wondering)

Infinitely many... We can write the above equivalently as follows, right?

$\begin{bmatrix}
2 & 3
\end{bmatrix} \begin{bmatrix}
x\\
y
\end{bmatrix}=6$

How can we justify then that there are infinitely many solutions? (Thinking)
 
evinda said:
Infinitely many... We can write the above equivalently as follows, right?

$\begin{bmatrix}
2 & 3
\end{bmatrix} \begin{bmatrix}
x\\
y
\end{bmatrix}=6$

How can we justify then that there are infinitely many solutions? (Thinking)

Right. (Nod)

If we have $m$ independent equations with $m$ variables, there is a unique solution.
We can tell because if $A$ is square, the fact that the rows are independent means that it's invertible.
So $Ax=b \Rightarrow x = A^{-1}b$.

Now that we have more than $m$ variables, we can for starters set them to zero, giving us a first solution.
And since we have a linear dependency in the columns, we can shift them around, giving us infinitely many solutions.
This corresponds to moving along a boundary line of the feasible region. (Thinking)
 

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