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Solve the system of linear equations using the Gauss-Jordan elimination method. $$ \begin{array}{rr} 2 x+3 y-6 z= & -11 \\ x-2 y+3 z= & 9 \\ 3 x+y=7 \end{array} $$

Short Answer

Expert verified
The solution to the system of equations using the Gauss-Jordan elimination method is \((x, y, z) = (4, 3, 5)\).

Step by step solution

01

Convert the system into an augmented matrix

First, we will write the given system of equations as an augmented matrix: \[ \left[ \begin{array}{ccc|c} 2 & 3 & -6 & -11 \\ 1 & -2 & 3 & 9 \\ 3 & 1 & 0 & 7 \end{array} \right] \]
02

Perform row operations to get the matrix into RREF

Our goal is to create a diagonal matrix through applying row operations. We'll start in the first column, working down the rows. In the end, we'll have a matrix in reduced row-echelon form, from which we can read the solution. First, we will swap rows 1 and 2 to get a 1 in the first row and first column: \[ \left[ \begin{array}{ccc|c} 1 & -2 & 3 & 9 \\ 2 & 3 & -6 & -11 \\ 3 & 1 & 0 & 7 \end{array} \right] \] Now, let's eliminate the first column in rows 2 and 3. We can do this by subtracting 2 times the first row from the second row and subtracting 3 times the first row from the third row: \[ \left[ \begin{array}{ccc|c} 1 & -2 & 3 & 9 \\ 0 & 7 & -12 & -29 \\ 0 & 5 & -9 & -20 \end{array} \right] \] Next, divide the second row by 7 to get a 1 in the second row and second column: \[ \left[ \begin{array}{ccc|c} 1 & -2 & 3 & 9 \\ 0 & 1 & \(-\frac{12}{7}\) & \(-\frac{29}{7}\) \\ 0 & 5 & -9 & -20 \end{array} \right] \] Now, let's eliminate the second column in row 3. We can do this by subtracting 5 times the second row from the third row: \[ \left[ \begin{array}{ccc|c} 1 & -2 & 3 & 9 \\ 0 & 1 & \(-\frac{12}{7}\) & \(-\frac{29}{7}\) \\ 0 & 0 & \(\frac{3}{7}\) & \(\frac{15}{7}\) \end{array} \right] \] Finally, divide the third row by \(\frac{3}{7}\) to get a 1 in the third row and third column: \[ \left[ \begin{array}{ccc|c} 1 & -2 & 3 & 9 \\ 0 & 1 & \(-\frac{12}{7}\) & \(-\frac{29}{7}\) \\ 0 & 0 & 1 & 5 \end{array} \right] \]
03

Back substitute to find the solution

Now that our matrix is in reduced row-echelon form, we can read the solution as (x, y, z) from the last column: From the third row: \(z = 5\) From the second row: \(y - \frac{12}{7}z = -\frac{29}{7}\) ⟹ \(y = 3\) From the first row: \(x - 2y + 3z = 9\) ⟹ \(x = 4\) Thus, the solution to the system of equations is \((x, y, z) = (4, 3, 5)\).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

System of Linear Equations
Understanding a system of linear equations is the foundational step in utilizing methods such as Gauss-Jordan elimination. A system of linear equations is a collection of two or more linear equations that are related by sharing common variables. For example, if we have equations that represent different line equations on a graph, their point of intersection is the solution to this system. When we're given a set of equations like:\[\begin{array}{rcl}2x + 3y - 6z & = & -11, \ x - 2y + 3z & = & 9, \ 3x + y & = & 7, \end{array}\]what we're searching for are the values of the variables \(x\), \(y\), and \(z\) that will satisfy all equations simultaneously. The Gauss-Jordan elimination method comes in handy for finding these values effectively.
Reduced Row-Echelon Form
The reduced row-echelon form (RREF) of a matrix is a simpler structure that exposes the relationships between the lines represented by the system of equations. In RREF, each variable is represented by a column that has a leading 1 (also known as a pivot), and all other entries in that column are zeros. The RREF is unique for each matrix and provides the clearest picture for the solution of a linear system. For instance, in the exercise we are looking at, once we've performed all the row operations necessary, the matrix in RREF allows us to straightforwardly interpret the values of our variables, making it a critically convenient form for problem-solving.
Augmented Matrix
An augmented matrix is a compact way to represent a system of linear equations. It consists of the coefficients of the variables in each equation and the constants from the right-hand side of the equations, all in one matrix. For our example, the augmented matrix is:\[\left[\begin{array}{ccc|c}2 & 3 & -6 & -11, \ 1 & -2 & 3 & 9, \ 3 & 1 & 0 & 7\end{array}\right]\]The vertical line in the matrix signifies where the equals sign would traditionally go in the equations, separating the coefficients from the constants. Using the augmented matrix structure streamlines the process of applying row operations to solve the system of equations, as it compiles all necessary information into one visual representation.
Row Operations
Row operations are the tools we utilize to manipulate an augmented matrix with the aim of achieving RREF, ultimately solving the system of equations. They are the backbone of methods like the Gauss-Jordan elimination. There are three elementary row operations:
  • Swapping two rows,
  • Multiplying a row by a non-zero scalar,
  • Adding a multiple of one row to another row.
In our exercise, we strategically apply these operations to transform the original matrix into one that meets the conditions of RREF. For example, we first swap rows to get a leading one in the top left corner. Then, we eliminate other entries in the first column by creating zero terms through addition or subtraction of multiples of the first row from the others. Progressing in this systematic way, from left to right and top to bottom, eventually grants us a matrix in RREF, which directly reveals the solution to our system.

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Most popular questions from this chapter

The Carver Foundation funds three nonprofit organizations engaged in alternate-energy research activities. From past data, the proportion of funds spent by each organization in research on solar energy, energy from harnessing the wind, and energy from the motion of ocean tides is given in the accompanying table. $$ \begin{array}{lccc} \hline && \text { Proportion of Money Spent } \\ & \text { Solar } & \text { Wind } & \text { Tides } \\ \hline \text { Organization I } & 0.6 & 0.3 & 0.1 \\ \hline \text { Organization II } & 0.4 & 0.3 & 0.3 \\ \hline \text { Organization III } & 0.2 & 0.6 & 0.2 \\ \hline \end{array} $$

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