Given A and b to the right, write the augmented matrix for the linear system that corresponds to the matrix equation Ax = b. Then solve the system and write the solution as a vector. Solve the system and write the solution as a vector. X₁ + x = x₂ X3 A = II 1 4 1 3 -4 -7 -2 2 2 b= Write the augmented matrix for the linear system that corresponds to the matrix equation Ax = b. 1 11

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### Solving Linear Systems with Augmented Matrices

#### Problem Statement

**Given matrices \(A\) and \(b\):**

\[ A = \begin{bmatrix} 
1 & 4 & -2 \\
1 & 3 & 2 \\
-4 & -7 & 2 
\end{bmatrix}, \quad b = \begin{bmatrix} 
1 \\
6 \\
11 
\end{bmatrix} \]

**Write the augmented matrix for the linear system that corresponds to the matrix equation \(Ax = b\). Then solve the system and write the solution as a vector.**

---

#### Steps

1. **Write the augmented matrix for the linear system that corresponds to the matrix equation \(Ax = b\).**

   The augmented matrix combines matrix \(A\) with vector \(b\):

   \[
   \left[ 
   \begin{array}{ccc|c}
   1 & 4 & -2 & 1 \\
   1 & 3 & 2 & 6 \\
   -4 & -7 & 2 & 11 
   \end{array}
   \right]
   \]

2. **Solve the system and write the solution as a vector.**

   To solve the system, we perform row operations to convert the augmented matrix to row echelon form, followed by back-substitution if necessary. Since the details of elementary row operations are not provided here, let's denote the solution vector as:

   \[
   x = \begin{bmatrix}
   x_1 \\
   x_2 \\
   x_3 
   \end{bmatrix} = \begin{bmatrix}
   \boxed{} \\
   \boxed{} \\
   \boxed{} 
   \end{bmatrix}
   \]

   The actual values for \(x_1\), \(x_2\), and \(x_3\) should be computed through suitable row operations or use of computational tools dedicated to solving systems of linear equations.

##### Note
Remember that solving a linear system involves converting the augmented matrix to a form where the solutions for the variables can be clearly identified, often using methods such as Gaussian elimination or back-substitution. The solution should be verified by substituting back into the original equations to ensure correctness.

---

End of educational content.
Transcribed Image Text:### Solving Linear Systems with Augmented Matrices #### Problem Statement **Given matrices \(A\) and \(b\):** \[ A = \begin{bmatrix} 1 & 4 & -2 \\ 1 & 3 & 2 \\ -4 & -7 & 2 \end{bmatrix}, \quad b = \begin{bmatrix} 1 \\ 6 \\ 11 \end{bmatrix} \] **Write the augmented matrix for the linear system that corresponds to the matrix equation \(Ax = b\). Then solve the system and write the solution as a vector.** --- #### Steps 1. **Write the augmented matrix for the linear system that corresponds to the matrix equation \(Ax = b\).** The augmented matrix combines matrix \(A\) with vector \(b\): \[ \left[ \begin{array}{ccc|c} 1 & 4 & -2 & 1 \\ 1 & 3 & 2 & 6 \\ -4 & -7 & 2 & 11 \end{array} \right] \] 2. **Solve the system and write the solution as a vector.** To solve the system, we perform row operations to convert the augmented matrix to row echelon form, followed by back-substitution if necessary. Since the details of elementary row operations are not provided here, let's denote the solution vector as: \[ x = \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix} = \begin{bmatrix} \boxed{} \\ \boxed{} \\ \boxed{} \end{bmatrix} \] The actual values for \(x_1\), \(x_2\), and \(x_3\) should be computed through suitable row operations or use of computational tools dedicated to solving systems of linear equations. ##### Note Remember that solving a linear system involves converting the augmented matrix to a form where the solutions for the variables can be clearly identified, often using methods such as Gaussian elimination or back-substitution. The solution should be verified by substituting back into the original equations to ensure correctness. --- End of educational content.
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