Equivalent minimum linear program

In summary: R}^3} \min \hspace{0.1 in}&c^Tx \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*} which is the original objective function without the constant term. In summary, the correct equivalent linear minimization problem is to remove the constant term from the original objective function.
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Homework Statement



Suppose you are given the following affine minimization problem.

\begin{align*} \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in}&c^Tx+3c^Tc \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

Which of the following is an equivalent linear minimization problem?

\begin{align*} c^T \big( \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in} &x + 3c \big ) \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

\begin{align*} \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in}&c^T(x +3c)\\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

\begin{align*}3c^Tc+ \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in}&c^Tx \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

\begin{align*} \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in}&c^Tx \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

Homework Equations


The Attempt at a Solution


I guessed and was right, the answer is


\begin{align*}3c^Tc+ \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in}&c^Tx \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

However, I don't feel comfortable and it was more an educated guess, instead of firm understanding. I figured that ##3c^Tc## would not affect the minimum since it is just an additive, but how should I approach these sorts of problems? Why are the other choices incorrect?
 
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To approach these types of problems, it is important to understand the properties of affine and linear functions. An affine function is a function that can be written as ##f(x) = Ax + b##, where ##A## is a matrix and ##b## is a vector. A linear function is a special case of an affine function where ##b = 0##.

In this problem, we are given an affine minimization problem, which means that the objective function can be written as ##c^Tx + 3c^Tc##. We are looking for an equivalent linear minimization problem, which means that the objective function should only have a linear term, i.e. ##c^Tx##.

Let's look at each of the given options and see why they are incorrect:

1. \begin{align*} c^T \big( \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in} &x + 3c \big ) \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

This option is incorrect because the objective function is not linear. The term ##3c## is not a constant and therefore cannot be pulled out of the minimization.

2. \begin{align*} \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in}&c^T(x +3c)\\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

This option is incorrect because the objective function is not linear. The term ##x + 3c## is not a linear term.

3. \begin{align*}3c^Tc+ \underset{x \in \mathbb{R}^3} \min \hspace{0.1 in}&c^Tx \\ &\text{subject to} \hspace{0.1 in} Ax \leq b \end{align*}

This option is incorrect because the objective function is not equivalent to the given affine minimization problem. The term ##3c^Tc## is a constant and does not affect the minimization.

Therefore, the correct option is \begin{align*} \underset{x \in \math
 

1. What is an equivalent minimum linear program?

An equivalent minimum linear program is a mathematical optimization problem that has the same objective function and constraints as another linear program, but may have a different set of decision variables.

2. How is an equivalent minimum linear program different from a regular linear program?

An equivalent minimum linear program has the same optimal solution as the original linear program, but may have a different formulation. This means that it may use different decision variables, but it will still have the same optimal value for the objective function and satisfy the same constraints.

3. What is the importance of finding an equivalent minimum linear program?

Finding an equivalent minimum linear program can be useful in simplifying and optimizing the solution process for a linear program. It allows for the use of different decision variables or constraints to potentially improve the efficiency of finding the optimal solution.

4. How can an equivalent minimum linear program be identified?

An equivalent minimum linear program can be identified by comparing the objective function and constraints of the original linear program to the equivalent one. They should have the same optimal value and satisfy the same constraints, but may have different variables or constraints.

5. What are some common techniques used to generate equivalent minimum linear programs?

Some common techniques used to generate equivalent minimum linear programs include converting between primal and dual forms, applying variable substitutions, and using linear algebra methods such as row reduction and matrix transformations.

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